Publications by Miguel Hernández-Cabronero

    2019

    • I. Blanes, M. Hernández-Cabronero, J. Serra-Sagristà, M.W. Marcellin,
      "Lower Bounds on the Redundancy of Huffman Codes with Known and Unknown Probabilities",
      IEEE Access, 2019.


      (Full text download not available)

      @article { 2019_blanes_lower,
      	author = {I. Blanes and M. Hern{\'a}ndez-Cabronero and J. Serra-Sagrist{\`a}
      and M.W. Marcellin},
      	title = {Lower Bounds on the Redundancy of Huffman Codes with Known and Unknown
      Probabilities},
      	journal = {IEEE Access},
      	year = {2019},
      }
      
      
      
    • Joan Bartrina-Rapesta, Joan Serra-Sagristà, M.W. Marcellin, M. Hernández-Cabronero,
      "A Novel Rate-Control for Predictive Image Coding with Constant Quality",
      IEEE Access, pp.1-3, 2019.


      (Full text download not available)

      @article { 2019_bartrina_ratecontrol,
      	author = {Joan Bartrina-Rapesta and Joan Serra-Sagrist{\`a} and M.W. Marcellin
      and M. Hern{\'a}ndez-Cabronero},
      	title = {A Novel Rate-Control for Predictive Image Coding with Constant
      Quality},
      	journal = {IEEE Access},
      	pages = {1-3},
      	doi = {10.1109/ACCESS.2019.2931442},
      	year = {2019},
      }
      
      
      
    • Miguel Hernández-Cabronero, David Vilaseca, Guillermo Becker, Emilio Tylson, Joan Serra-Sagristà,
      "Effect of Lightweight Image Compression on CNN-based Image Segmentation",
      Grenoble NewSpace Week (GNSW), pp.31, apr 2019.

      @inproceedings { 2019_hernandez_effect,
      	author = {Miguel Hern{\'a}ndez-Cabronero and David Vilaseca and Guillermo
      Becker and Emilio Tylson and Joan Serra-Sagrist{\`a}},
      	title = {Effect of Lightweight Image Compression on CNN-based Image
      Segmentation},
      	booktitle = {Grenoble NewSpace Week (GNSW)},
      	year = {2019},
      	month = {apr},
      	pages = {31},
      }
      
      
      
    • Ian Blanes, Aaron Kiely, Miguel Hernández Cabronero, Joan Serra-Sagristà,
      "Performance Impact of Parameter Tuning on the CCSDS-123.0-B-2 Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression Standard",
      MDPI Remote Sensing, vol.11, no.11, pp.1390, 2019.


      (Also available at the publisher's site)

      This article studies the performance impact related to different parameter choices for the new CCSDS-123.0-B-2 Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression standard. This standard supersedes CCSDS-123.0-B-1 and extends it by incorporating a new near-lossless compression capability, as well as other new features. This article studies the coding performance impact of different choices for the principal parameters of the new extensions, in addition to reviewing related parameter choices for existing features. Experimental results include data from 16 different instruments with varying detector types, image dimensions, number of spectral bands, bit depth, level of noise, level of calibration, and other image characteristics. Guidelines are provided on how to adjust the parameters in relation to their coding performance impact.
      @article { Blanes19rs,
      	author = {Ian Blanes and Aaron Kiely and Miguel Hern{\'a}ndez Cabronero and
      Joan Serra-Sagrist{\`a}},
      	title = {Performance Impact of Parameter Tuning on the CCSDS-123.0-B-2
      Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image
      Compression Standard},
      	journal = {MDPI Remote Sensing},
      	pages = {1390},
      	volume = {11},
      	number = {11},
      	doi = {10.3390/rs11111390},
      	year = {2019},
      	url = {https://www.mdpi.com/2072-4292/11/11/1390},
      	abstract = {This article studies the performance impact related to different
      parameter choices for the new CCSDS-123.0-B-2 Low-Complexity Lossless and
      Near-Lossless Multispectral and Hyperspectral Image Compression standard. This
      standard supersedes CCSDS-123.0-B-1 and extends it by incorporating a new
      near-lossless compression capability, as well as other new features. This
      article studies the coding performance impact of different choices for the
      principal parameters of the new extensions, in addition to reviewing related
      parameter choices for existing features. Experimental results include data from
      16 different instruments with varying detector types, image dimensions, number
      of spectral bands, bit depth, level of noise, level of calibration, and other
      image characteristics. Guidelines are provided on how to adjust the parameters
      in relation to their coding performance impact.}
      }
      
      
      
    • Miguel Hernández-Cabronero, Victor Sanchez, Ian Blanes, Francesc Aulí-Llinàs, Michael W. Marcellin, Joan Serra-Sagristà,
      "Mosaic-Based Color-Transform Optimization for the Lossy and Lossy-to-Lossless compression of Pathology Whole-Slide Images",
      IEEE Transactions on Medical Imaging, vol.38, no.10, pp.21-32, 2019.


      (Also available at the publisher's site)

      The use of whole-slide images (WSIs) in pathology entails stringent storage and transmission requirements because of their huge dimensions. Therefore, image compression is an essential tool to enable efficient access to these data. In particular, color transforms are needed to exploit the very high degree of inter-component correlation and obtain competitive compression performance. Even though state-of-the-art color transforms remove some redundancy, they disregard important details of the compression algorithm applied after the transform. Therefore, their coding performance is not optimal. We propose an optimization method called Mosaic Optimization for designing irreversible and reversible color transforms simultaneously optimized for any given WSI and the subsequent compression algorithm. Mosaic Optimization is designed to attain reasonable computational complexity and enable continuous scanner operation. Exhaustive experimental results indicate that, for JPEG 2000 at identical compression ratios, the optimized transforms yield images more similar to the original than other state-of-the-art transforms. Specifically, irreversible optimized transforms outperform the Karhunen-Loève Transform (KLT) in terms of PSNR (up to 1.1 dB), the HDR-VDP-2 visual distortion metric (up to 3.8 dB) and accuracy of computer-aided nuclei detection tasks (F1 score up to 0.04 higher). Additionally, reversible optimized transforms achieve PSNR, HDR-VDP-2 and nuclei detection accuracy gains of up to 0.9 dB, 7.1 dB and 0.025, respectively, when compared to the reversible color transform (RCT) in lossy-to-lossless compression regimes.
      @article { Hernandez19TMI,
      	author = {Miguel Hern{\'a}ndez-Cabronero and Victor Sanchez and Ian Blanes and
      Francesc Aul{\'i}-Llin{\`a}s and Michael W. Marcellin and Joan
      Serra-Sagrist{\`a}},
      	title = {Mosaic-Based Color-Transform Optimization for 
      the Lossy and Lossy-to-Lossless compression of Pathology Whole-Slide Images},
      	journal = {IEEE Transactions on Medical Imaging},
      	year = {2019},
      	volume = {38},
      	number = {10},
      	pages = {21-32},
      	url = {https://dx.doi.org/10.1109/TMI.2018.2852685},
      	abstract = {The use of whole-slide images (WSIs) in pathology entails stringent
      storage and transmission requirements because of their huge dimensions.
      Therefore, image compression is an essential tool to enable efficient access to
      these data. In particular, color transforms are needed to exploit the very high
      degree of inter-component correlation and obtain competitive compression
      performance. Even though state-of-the-art color transforms remove some
      redundancy, they disregard important details of the compression algorithm
      applied after the transform. Therefore, their coding performance is not optimal.
      We propose an optimization method called Mosaic Optimization for designing
      irreversible and reversible color transforms simultaneously optimized for any
      given WSI and the subsequent compression algorithm. Mosaic Optimization is
      designed to attain reasonable computational complexity and enable continuous
      scanner operation. Exhaustive experimental results indicate that, for JPEG 2000
      at identical compression ratios, the optimized transforms yield images more
      similar to the original than other state-of-the-art transforms. Specifically,
      irreversible optimized transforms outperform the Karhunen-Loève Transform (KLT)
      in terms of PSNR (up to 1.1 dB), the HDR-VDP-2 visual distortion metric (up to
      3.8 dB) and accuracy of computer-aided nuclei detection tasks (F1 score up to
      0.04 higher). Additionally, reversible optimized transforms achieve PSNR,
      HDR-VDP-2 and nuclei detection accuracy gains of up to 0.9 dB, 7.1 dB and 0.025,
      respectively, when compared to the reversible color transform (RCT) in
      lossy-to-lossless compression regimes.}
      }
      
      
      

    2018

    • A. Kiely, M. Klimesh, I. Blanes, J. Ligo, E. Magli, N. Aranki, M. Burl, R. Camarero, M. Cheng, S. Dolinar, D. Dolman, G. Flesch, H. Ghassemi, M. Gilbert, Miguel Hernández-Cabronero, D. Keymeulen, M. Le, H. Luong, C. McGuiness, G. Moury, T. Pham, M. Plintovic, F. Sala, L. Santos, A. Schaar, J. Serra-Sagristà, S. Shin, B. Sundlie, D. Valsesia, R. Vitulli, E. Wong, W. Wu, H. Xie, H. Zhou,
      "The new CCSDS Standard for Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression",
      In Proceedings of the ESA On-Board Payload Data Compression Workshop, OBPDC, September 2018.


      (Also available at the publisher's site)

      This paper describes the emerging Issue 2 of the CCSDS-123.0-B standard for low-complexity compression of multispectral and hyperspectral imagery, focusing on its new features and capabilities. Most significantly, this new issue incorporates a closed-loop quantization scheme to provide near-lossless compression capability while still supporting lossless compression, and introduces a new entropy coding option that provides better compression of low-entropy data.
      @inproceedings { KielyOBPDC18,
      	author = {A. Kiely and M. Klimesh and I. Blanes and J. Ligo and E. Magli and N.
      Aranki and M. Burl and R. Camarero and M. Cheng and S. Dolinar and D. Dolman and
      G. Flesch and H. Ghassemi and M. Gilbert and Miguel Hern{\'a}ndez-Cabronero and
      D. Keymeulen and M. Le and H. Luong and C. McGuiness and G. Moury and T. Pham
      and M. Plintovic and F. Sala and L. Santos and A. Schaar and J.
      Serra-Sagrist{\`a} and S. Shin and B. Sundlie and D. Valsesia and R. Vitulli and
      E. Wong and W. Wu and H. Xie and H. Zhou},
      	title = {The new CCSDS Standard for Low-Complexity Lossless and Near-Lossless
      Multispectral and Hyperspectral Image Compression},
      	booktitle = {In Proceedings of the ESA On-Board Payload Data Compression
      Workshop, OBPDC},
      	year = {2018},
      	month = {September},
      	url = {https://ntrs.nasa.gov/search.jsp?R=20180006784},
      	abstract = {This paper describes the emerging Issue 2 of the CCSDS-123.0-B
      standard for low-complexity compression of multispectral and hyperspectral
      imagery, focusing on its new features and capabilities. Most significantly, this
      new issue incorporates a closed-loop quantization scheme to provide
      near-lossless compression capability while still supporting lossless
      compression, and introduces a new entropy coding option that provides better
      compression of low-entropy data.}
      }
      
      
      
    • J. Portell, Ian Blanes, Miguel Hernández-Cabronero, Joan Serra-Sagristà, R. Iudica, A. G. Villafranca,
      "Prepending Spectral Decorrelating Transforms to FAPEC: A competitive High-Performance approach for Remote Sensing Data Compression",
      In Proceedings of the ESA On-Board Payload Data Compression Workshop, OBPDC, September 2018.


      (Full text download not available)

      @inproceedings { PortellOBPDC18,
      	author = {J. Portell and Ian Blanes and Miguel Hern{\'a}ndez-Cabronero and Joan
      Serra-Sagrist{\`a} and R. Iudica and A. G. Villafranca},
      	title = {Prepending Spectral Decorrelating Transforms to FAPEC: A competitive
      High-Performance approach for Remote Sensing Data Compression},
      	booktitle = {In Proceedings of the ESA On-Board Payload Data Compression
      Workshop, OBPDC},
      	year = {2018},
      	month = {September},
      }
      
      
      
    • Ian Blanes, Aaron Kiely, Miguel Hernández-Cabronero, Joan Serra-Sagristà,
      "Performance Impact of Parameter Tuning on the Emerging CCSDS-123.0-B-2 Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression Standard",
      In Proceedings of the ESA On-Board Payload Data Compression Workshop, OBPDC, September 2018.


      (Full text download not available)

      @inproceedings { BlanesOBPDC18,
      	author = {Ian Blanes and Aaron Kiely and Miguel Hern{\'a}ndez-Cabronero and
      Joan Serra-Sagrist{\`a}},
      	title = {Performance Impact of Parameter Tuning on the Emerging CCSDS-123.0-B-2
      Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image
      Compression Standard},
      	booktitle = {In Proceedings of the ESA On-Board Payload Data Compression
      Workshop, OBPDC},
      	year = {2018},
      	month = {September},
      }
      
      
      
    • Victor Sanchez, Miguel Hernández-Cabronero,
      "Graph-based Rate Control in Pathology Imaging with Lossless Region of Interest Coding",
      IEEE Transactions on Medical Imaging, vol.37, no.10, pp.2211-2223, 2018.


      (Full text may be available at the publisher's site)

      The increasing availability of digital pathology images has motivated the design of tools to foster multidisciplinary collaboration among researchers, pathologists, and computer scientists. Telepathology plays an important role in the development of collaborative tools, as it facilities the transmission and access of pathology images by multiple users. However, the huge file size associated with pathology images usually prevents fully exploiting the potential of collaborative telepathology systems. Within this context, rate control (RC) is an important tool that allows meeting storage and bandwidth requirements by controlling the bit rate of the coded image. In this paper, we propose a novel graph-based RC algorithm with lossless region of interest (RoI) coding of pathology images. The algorithm, which is designed for block-based predictive transform coding methods, compresses the non-RoI in a lossy manner according to a target bit rate and the RoI in a lossless manner. It employs a graph where each node represents a constituent block of the image to be coded. By incorporating information about the coding cost similarities of blocks into the graph, a graph kernel is used to distribute a target bit budget among the non-RoI blocks. In order to increase RC accuracy, the algorithm uses a rate-lambda (R-$\lambda$) model to approximate the slope of the rate-distortion curve of the non-RoI, and a graph-based approach to guarantee that the target bit rate is accurately attained. The algorithm is implemented in the HEVC standard and tested over a wide range of pathology images with multiple RoIs. Evaluation results show that it outperforms other state-of-the-art methods designed for single images by very accurately attaining the target bit rate of the non-RoI.
      @article { Sanchez18TMI,
      	author = {Victor Sanchez and Miguel Hern{\'a}ndez-Cabronero},
      	title = {Graph-based Rate Control in Pathology Imaging with Lossless Region of
      Interest Coding},
      	journal = {IEEE Transactions on Medical Imaging},
      	year = {2018},
      	volume = {37},
      	number = {10},
      	pages = {2211-2223},
      	url =
      {https://ieeexplore.ieee.org/document/8340839?arnumber=8340839&source=authoralert},
      	abstract = {The increasing availability of digital pathology images has
      motivated the design of tools to foster multidisciplinary collaboration among
      researchers, pathologists, and computer scientists. Telepathology plays an
      important role in the development of collaborative tools, as it facilities the
      transmission and access of pathology images by multiple users. However, the huge
      file size associated with pathology images usually prevents fully exploiting the
      potential of collaborative telepathology systems. Within this context, rate
      control (RC) is an important tool that allows meeting storage and bandwidth
      requirements by controlling the bit rate of the coded image. In this paper, we
      propose a novel graph-based RC algorithm with lossless region of interest (RoI)
      coding of pathology images. The algorithm, which is designed for block-based
      predictive transform coding methods, compresses the non-RoI in a lossy manner
      according to a target bit rate and the RoI in a lossless manner. It employs a
      graph where each node represents a constituent block of the image to be coded.
      By incorporating information about the coding cost similarities of blocks into
      the graph, a graph kernel is used to distribute a target bit budget among the
      non-RoI blocks. In order to increase RC accuracy, the algorithm uses a
      rate-lambda (R-$\lambda$) model to approximate the slope of the rate-distortion
      curve of the non-RoI, and a graph-based approach to guarantee that the target
      bit rate is accurately attained. The algorithm is implemented in the HEVC
      standard and tested over a wide range of pathology images with multiple RoIs.
      Evaluation results show that it outperforms other state-of-the-art methods
      designed for single images by very accurately attaining the target bit rate of
      the non-RoI.}
      }
      
      
      
    • Francesc Aulí-Linàs, Michael W. Marcellin, Victor Sanchez, Joan Bartrina-Rapesta, Miguel Hernández-Cabronero,
      "Dual Link Image Coding for Earth Observation Satellites",
      IEEE Transactions on Geoscience and Remote Sensing, vol.56, no.9, pp.5083-5096, 2018.


      (Full text may be available at the publisher's site)

      The conventional strategy to download images captured by satellites is to compress the data on board and then transmit them via the downlink. It often happens that the capacity of the downlink is too small to accommodate all the acquired data, so the images are trimmed and/or transmitted through lossy regimes. This paper introduces a coding system that increases the amount and quality of the downloaded imaging data. The main insight of this work is to use both the uplink and the downlink to code the images. The uplink is employed to send reference information to the satellite so that the on-board coding system can achieve higher efficiency. This reference information is computed on the ground, possibly employing extensive data and computational resources. The proposed system is called dual link image coding. As it is devised in this work, it is suitable for Earth observation satellites with polar orbits. Experimental results obtained for datasets acquired by the Landsat 8 satellite indicate significant coding gains with respect to conventional methods.
      @article { Auli18DLIC,
      	author = {Francesc Aul{\'i}-Lin{\`a}s and Michael W. Marcellin and Victor
      Sanchez and Joan Bartrina-Rapesta and Miguel Hern{\'a}ndez-Cabronero},
      	title = {Dual Link Image Coding for Earth Observation Satellites},
      	journal = {IEEE Transactions on Geoscience and Remote Sensing},
      	volume = {56},
      	number = {9},
      	pages = {5083-5096},
      	year = {2018},
      	url = {https://ieeexplore.ieee.org/document/8307760},
      	abstract = {The conventional strategy to download images captured by satellites
      is to compress the data on board and then transmit them via the downlink. It
      often happens that the capacity of the downlink is too small to accommodate all
      the acquired data, so the images are trimmed and/or transmitted through lossy
      regimes. This paper introduces a coding system that increases the amount and
      quality of the downloaded imaging data. The main insight of this work is to use
      both the uplink and the downlink to code the images. The uplink is employed to
      send reference information to the satellite so that the on-board coding system
      can achieve higher efficiency. This reference information is computed on the
      ground, possibly employing extensive data and computational resources. The
      proposed system is called dual link image coding. As it is devised in this work,
      it is suitable for Earth observation satellites with polar orbits. Experimental
      results obtained for datasets acquired by the Landsat 8 satellite indicate
      significant coding gains with respect to conventional methods.}
      }
      
      
      
    • Miguel Hernández-Cabronero, Michael W. Marcellin, Ian Blanes, Joan Serra-Sagristà,
      "Lossless Compression of Color Filter Array Mosaic Images with Visualization via JPEG 2000",
      IEEE Transactions on Multimedia, vol.20, no.2, pp.257-270, 2018.


      (Also available at the publisher's site)

      Digital cameras have become ubiquitous for amateur and professional applications. The raw images captured by digital sensors typically take the form of color filter array (CFA) mosaic images, which must be "developed" (via digital signal processing) before they can be viewed. Photographers and scientists often repeat the "development process" using different parameters to obtain images suitable for different purposes. Since the development process is generally not invertible, it is commonly desirable to store the raw (or undeveloped) mosaic images indefinitely. Uncompressed mosaic image file sizes can be more than 30 times larger than those of developed images stored in JPEG format. Data compression is thus of interest. Several compression methods for mosaic images have been proposed in the literature. However, they all require a custom decompressor followed by development-specific software to generate a displayable image. In this paper, a novel compression pipeline is proposed that removes these requirements. Specifically, mosaic images can be losslessly recovered from the resulting compressed files, and, more significantly, images can be directly viewed (decompressed and developed) using only a JPEG~2000 compliant image viewer. Experiments reveal that the proposed pipeline attains excellent visual quality, while providing compression performance competitive to that of state-of-the-art compression algorithms for mosaic images.
      @article { Hernandez18TMM,
      	author = {Miguel Hern{\'a}ndez-Cabronero and Michael W. Marcellin and Ian
      Blanes and Joan Serra-Sagrist{\`a}},
      	title = {Lossless Compression of Color Filter Array Mosaic Images with
      Visualization via JPEG 2000},
      	journal = {IEEE Transactions on Multimedia},
      	year = {2018},
      	volume = {20},
      	number = {2},
      	pages = {257-270},
      	url = {http://dx.doi.org/10.1109/TMM.2017.2741426},
      	supplementary_url =
      {http://deic.uab.cat/~mhernandez/media/imagesets/bayer_cfa_multimedia_materials.zip},
      	abstract = {Digital cameras have become ubiquitous for amateur and professional
      applications. The raw images captured by digital sensors typically take the form
      of color filter array (CFA) mosaic images, which must be "developed" (via
      digital signal processing) before they can be viewed. Photographers and
      scientists often repeat the "development process" using different parameters to
      obtain images suitable for different purposes. Since the development process is
      generally not invertible, it is commonly desirable to store the raw (or
      undeveloped) mosaic images indefinitely. Uncompressed mosaic image file sizes
      can be more than 30 times larger than those of developed images stored in JPEG
      format. Data compression is thus of interest. Several compression methods for
      mosaic images have been proposed in the literature. However, they all require a
      custom decompressor followed by development-specific software to generate a
      displayable image. In this paper, a novel compression pipeline is proposed that
      removes these requirements. Specifically, mosaic images can be losslessly
      recovered from the resulting compressed files, and, more significantly, images
      can be directly viewed (decompressed and developed) using only a JPEG~2000
      compliant image viewer. Experiments reveal that the proposed pipeline attains
      excellent visual quality, while providing compression performance competitive to
      that of state-of-the-art compression algorithms for mosaic images.}
      }
      
      
      
    • Feng Liu, Yuzhang Lin, M. Hernández-Cabronero, Eze Ahanonu, Michael W. Marcellin, Amit Ashok, Ali Bilgin,
      "A Visual Discrimination Model for JPEG2000 Compression",
      In Proceedings of the IEEE Data Compression Conference, DCC, pp.424-424, March 2018.


      (Full text may be available at the publisher's site)

      @inproceedings { Prangnell17DCC,
      	author = {Feng Liu and Yuzhang Lin and M. Hern{\'a}ndez-Cabronero and Eze
      Ahanonu and Michael W. Marcellin and Amit Ashok and Ali Bilgin},
      	title = {A Visual Discrimination Model for JPEG2000 Compression},
      	booktitle = {In Proceedings of the IEEE Data Compression Conference, DCC},
      	pages = {424-424},
      	year = {2018},
      	month = {March},
      	url = {https://ieeexplore.ieee.org/abstract/document/8416641},
      }
      
      
      

    2017

    • Feng Liu, Miguel Hernández-Cabronero, Victor Sanchez, Michael W. Marcellin, Ali Bilgin,
      "The Current Role of Image Compression Standards in Medical Imaging",
      MDPI Information, vol.8, no.4, 2017.


      (Also available at the publisher's site)

      With the increasing utilization of medical imaging in clinical practice and the growing dimensions of data volumes generated by various medical imaging modalities, the distribution, storage, and management of digital medical image data sets requires data compression. Over the past few decades, several image compression standards have been proposed by international standardization organizations. This paper discusses the current status of these image compression standards in medical imaging applications together with some of the legal and regulatory issues surrounding the use of compression in medical settings.
      @article { Feng17MDPI,
      	author = {Feng Liu and Miguel Hern{\'a}ndez-Cabronero and Victor Sanchez and
      Michael W. Marcellin and Ali Bilgin},
      	title = {The Current Role of Image Compression Standards in Medical Imaging},
      	journal = {MDPI Information},
      	year = {2017},
      	volume = {8},
      	number = {4},
      	url = {http://dx.doi.org/10.3390/info8040131},
      	abstract = {With the increasing utilization of medical imaging in clinical
      practice and the growing dimensions of data volumes generated by various medical
      imaging modalities, the distribution, storage, and management of digital medical
      image data sets requires data compression. Over the past few decades, several
      image compression standards have been proposed by international standardization
      organizations. This paper discusses the current status of these image
      compression standards in medical imaging applications together with some of the
      legal and regulatory issues surrounding the use of compression in medical
      settings.}
      }
      
      
      
    • Sara Álvarez-Cortes, Naoufal Amrani, Miguel Hernández-Cabronero, Joan Serra-Sagristà,
      "Progressive Lossy-to-Lossless Coding of Hyperspectral Images through Regression Wavelet Analysis",
      Taylor & Francis International Journal of Remote Sensing, vol.39, no.7, pp.1-21, 2017.


      (Full text may be available at the publisher's site)

      @article { Alvarez17TFIJRS,
      	author = {Sara {\'A}lvarez-Cortes and Naoufal Amrani and Miguel
      Hern{\'a}ndez-Cabronero and Joan Serra-Sagrist{\`a}},
      	title = {Progressive Lossy-to-Lossless Coding of Hyperspectral Images through
      Regression Wavelet Analysis},
      	journal = {Taylor & Francis International Journal of Remote Sensing},
      	pages = {1-21},
      	year = {2017},
      	volume = {39},
      	number = {7},
      	url = {http://dx.doi.org/10.1080/01431161.2017.1343515},
      }
      
      
      
    • Lee Prangnell, Miguel Hernández-Cabronero, Victor Sanchez,
      "Coding Block-Level Perceptual Video Coding for 4:4:4 Data in HEVC",
      In Proceedings of the IEEE International Conference on Image Processing, ICIP, pp.2488-2492, September 2017.


      (Full text download not available)

      There is an increasing consumer demand for high bit-depth 4:4:4 HD video data playback due to its superior perceptual visual quality compared with standard 8-bit subsampled 4:2:0 video data. Due to vast file sizes and associated bitrates, it is desirable to compress raw high bit-depth 4:4:4 HD video sequences as much as possible without incurring a discernible decrease in visual quality. In this paper, we propose a Coding Bock (CB)-level adaptive perceptual video compression method for HEVC named Full Color Perceptual Quantization (FCPQ). FCPQ is designed to perceptually adjust the Quantization Parameter (QP) at the CB level -i.e., the luma CB and the chroma Cb and Cr CBs- according to the variances of pixel data in each CB. FCPQ is based on the default adaptive perceptual quantization method in the HEVC, known as AdaptiveQP. AdaptiveQP perceptually adjusts the QP of an entire 2Nx2N CU (i.e., one QP applied to the Y CB, the Cb CB and the Cr CB) based only on the spatial activity of the luma CB; it does not account for the spatial activity of the chroma Cb and Cr CBs. This has the potential to affect coding performance, primarily because the variance of pixel data in a luma CB is notably different from the variances of pixel data in chroma Cb and Cr CBs. FCPQ addresses this problem. In terms of coding performance, FCPQ achieves BD-Rate improvements of up to 39.5\% (Y), 16\% (Cb) and 29.9\% (Cr) compared with AdaptiveQP.
      @inproceedings { Prangnell17ICIP,
      	author = {Lee Prangnell and Miguel Hern{\'a}ndez-Cabronero and Victor Sanchez},
      	title = {Coding Block-Level Perceptual Video Coding for 4:4:4 Data in HEVC},
      	booktitle = {In Proceedings of the IEEE International Conference on Image
      Processing, ICIP},
      	year = {2017},
      	pages = {2488-2492},
      	month = {September},
      	abstract = {There is an increasing consumer demand for high bit-depth 4:4:4 HD
      video data playback due to its superior perceptual visual quality compared with
      standard 8-bit subsampled 4:2:0 video data. Due to vast file sizes and
      associated bitrates, it is desirable to compress raw high bit-depth 4:4:4 HD
      video sequences as much as possible without incurring a discernible decrease in
      visual quality. In this paper, we propose a Coding Bock (CB)-level adaptive
      perceptual video compression method for HEVC named Full Color Perceptual
      Quantization (FCPQ). FCPQ is designed to perceptually adjust the Quantization
      Parameter (QP) at the CB level -i.e., the luma CB and the chroma Cb and Cr CBs-
      according to the variances of pixel data in each CB. FCPQ is based on the
      default adaptive perceptual quantization method in the HEVC, known as
      AdaptiveQP. AdaptiveQP perceptually adjusts the QP of an entire 2Nx2N CU (i.e.,
      one QP applied to the Y CB, the Cb CB and the Cr CB) based only on the spatial
      activity of the luma CB; it does not account for the spatial activity of the
      chroma Cb and Cr CBs. This has the potential to affect coding performance,
      primarily because the variance of pixel data in a luma CB is notably different
      from the variances of pixel data in chroma Cb and Cr CBs. FCPQ addresses this
      problem. In terms of coding performance, FCPQ achieves BD-Rate improvements of
      up to 39.5\% (Y), 16\% (Cb) and 29.9\% (Cr) compared with AdaptiveQP.}
      }
      
      
      
    • Lee Prangnell, Miguel Hernández-Cabronero, Victor Sanchez,
      "Cross-Color Channel Perceptually Adaptive Quantization for HEVC",
      In Proceedings of the IEEE Data Compression Conference, DCC, pp.456-456, April 2017.


      (Full text download not available)

      HEVC includes a Coding Unit (CU) level luminance-based perceptual quantization technique known as AdaptiveQP. AdaptiveQP perceptually adjusts the Quantization Parameter (QP) at the CU level based on the spatial activity of raw input video data in a luma Coding Block (CB). In this paper, we propose a novel cross-color channel adaptive quantization scheme which perceptually adjusts the CU level QP according to the spatial activity of raw input video data in the constituent luma and chroma CBs; i.e., the combined spatial activity across all three color channels (the Y, Cb and Cr channels). Our technique is evaluated in HM 16 with 4:4:4, 4:2:2 and 4:2:0 YCbCr JCT-VC test sequences. Both subjective and objective visual quality evaluations are undertaken during which we compare our method with AdaptiveQP. Our technique achieves considerable coding efficiency improvements, with maximum BD-Rate reductions of 15.9% (Y), 13.1% (Cr) and 16.1% (Cb) in addition to a maximum decoding time reduction of 11.0%.
      @inproceedings { Prangnell17DCC,
      	author = {Lee Prangnell and Miguel Hern{\'a}ndez-Cabronero and Victor Sanchez},
      	title = {Cross-Color Channel Perceptually Adaptive Quantization for HEVC},
      	booktitle = {In Proceedings of the IEEE Data Compression Conference, DCC},
      	pages = {456-456},
      	year = {2017},
      	month = {April},
      	abstract = {HEVC includes a Coding Unit (CU) level luminance-based perceptual
      quantization technique known as AdaptiveQP. AdaptiveQP perceptually adjusts the
      Quantization Parameter (QP) at the CU level based on the spatial activity of raw
      input video data in a luma Coding Block (CB). In this paper, we propose a novel
      cross-color channel adaptive quantization scheme which perceptually adjusts the
      CU level QP according to the spatial activity of raw input video data in the
      constituent luma and chroma CBs; i.e., the combined spatial activity across all
      three color channels (the Y, Cb and Cr channels). Our technique is evaluated in
      HM 16 with 4:4:4, 4:2:2 and 4:2:0 YCbCr JCT-VC test sequences. Both subjective
      and objective visual quality evaluations are undertaken during which we compare
      our method with AdaptiveQP. Our technique achieves considerable coding
      efficiency improvements, with maximum BD-Rate reductions of 15.9% (Y), 13.1%
      (Cr) and 16.1% (Cb) in addition to a maximum decoding time reduction of 11.0%.}
      }
      
      
      

    2016

    • Miguel Hernández-Cabronero, Victor Sanchez, Francesc Aulí-Llinàs, Joan Serra-Sagristà,
      "Fast MCT Optimization for the Compression of Whole-Slide Images",
      In Proceedings of the IEEE International Conference on Image Processing, ICIP, pp.2370-2374, September 2016.


      (Also available at the publisher's site)

      Lossy compression techniques based on multi-component transformation (MCT) can effectively enhance the storage and transmission of whole-slide images (WSIs) without adversely affecting subsequent diagnosis processes. Component transforms that are designed for other types of images or that do not take into account all aspects of the compression algorithm applied on the transformed components yield suboptimal coding performance. Recently, an MCT optimization framework adapted to the particularities of the input WSI and the following compression was proposed, yielding superior coding performance than the state of the art. However, its time complexity is too high for practical purposes. In this work FastOptimizeMCT, a fast version of this framework based on smart sampling of regions depicting tissue, is proposed. Exhaustive experimental evidence indicates that FastOptimizeMCT exhibits reasonable time complexity results -similar to that of scanning the WSIs- and coding performance that outperforms the KLT and the OST by 1.47 dB and 1.07 dB, respectively
      @inproceedings { Hernandez16ICIP,
      	author = {Miguel Hern{\'a}ndez-Cabronero and Victor Sanchez and Francesc
      Aul{\'i}-Llin{\`a}s and Joan Serra-Sagrist{\`a}},
      	title = {Fast MCT Optimization for the Compression of Whole-Slide Images},
      	booktitle = {In Proceedings of the IEEE International Conference on Image
      Processing, ICIP},
      	year = {2016},
      	pages = {2370-2374},
      	month = {September},
      	url = {http://ieeexplore.ieee.org/document/7532783/},
      	abstract = {Lossy compression techniques based on multi-component
      transformation (MCT) can effectively enhance the storage and transmission of
      whole-slide images (WSIs) without adversely affecting subsequent diagnosis
      processes. Component transforms that are designed for other types of images or
      that do not take into account all aspects of the compression algorithm applied
      on the transformed components yield suboptimal coding performance. Recently, an
      MCT optimization framework adapted to the particularities of the input WSI and
      the following compression was proposed, yielding superior coding performance
      than the state of the art. However, its time complexity is too high for
      practical purposes. In this work FastOptimizeMCT, a fast version of this
      framework based on smart sampling of regions depicting tissue, is proposed.
      Exhaustive experimental evidence indicates that FastOptimizeMCT exhibits
      reasonable time complexity results -similar to that of scanning the WSIs- and
      coding performance that outperforms the KLT and the OST by 1.47 dB and 1.07 dB,
      respectively}
      }
      
      
      
    • Sara Álvarez-Cortés, Naoufal Amrani, Miguel Hernández-Cabronero, Joan Serra-Sagristà,
      "Progressive-Lossy-to-Lossless Coding of Hyperspectral Images",
      In Proceedings of the ESA On-Board Payload Data Compression Workshop, OBPDC, September 2016.


      (Full text download not available)

      @inproceedings { Alvarez16,
      	author = {Sara {\'A}lvarez-Cort{\'e}s and Naoufal Amrani and Miguel
      Hern{\'a}ndez-Cabronero and Joan Serra-Sagrist{\`a}},
      	title = {Progressive-Lossy-to-Lossless Coding of Hyperspectral Images},
      	booktitle = {In Proceedings of the ESA On-Board Payload Data Compression
      Workshop, OBPDC},
      	year = {2016},
      	month = {September},
      }
      
      
      
    • Miguel Hernández-Cabronero, Victor Sanchez, Francesc Aulí-Llinàs, Joan Serra-Sagristà,
      "Lossy Compression of Natural HDR Content Based on Multi-Component Transform Optimization",
      In Proceedings of the IEEE Digital Media Industry and Academy Forum, DMIAF, pp.23-28, July 2016.


      (Also available at the publisher's site)

      Linear multi-component transforms (MCTs) are commonly employed for enhancing the coding performance for the compression of natural color images. Popular MCTs such as the RGB to Y'CbCr transform are not optimized specifically for any given input image. Data-dependent transforms such as the Karhunen-Loève Transform (KLT) or the Optimal Spectral Transform (OST) optimize some analytical criteria (e.g., the inter-component correlation or mutual information), but do not consider all aspects of the coding system applied to the transformed components. Recently, a framework that produces optimized MCTs dependent on the input image and the subsequent coding system was proposed for 8-bit pathology whole-slide images. This work extends this framework to higher bitdepths and investigate its performance for different types of high-dynamic range (HDR) contents. Experimental results indicate that the optimized MCTs yield average PSNR results 0.17%, 0.47% and 0.63% higher than those of the KLT for raw mosaic images, reconstructed HDR radiance scenes and color graded HDR contents, respectively.
      @inproceedings { Hernandez16DMIAF,
      	author = {Miguel Hern{\'a}ndez-Cabronero and Victor Sanchez and Francesc
      Aul{\'i}-Llin{\`a}s and Joan Serra-Sagrist{\`a}},
      	title = {Lossy Compression of Natural HDR Content Based on Multi-Component
      Transform Optimization},
      	booktitle = {In Proceedings of the IEEE Digital Media Industry and Academy
      Forum, DMIAF},
      	year = {2016},
      	pages = {23-28},
      	month = {July},
      	url = {http://ieeexplore.ieee.org/document/7574894/},
      	abstract = {Linear multi-component transforms (MCTs) are commonly employed for
      enhancing the coding performance for the compression of natural color images.
      Popular MCTs such as the RGB to Y'CbCr transform are not optimized specifically
      for any given input image. Data-dependent transforms such as the Karhunen-Loève
      Transform (KLT) or the Optimal Spectral Transform (OST) optimize some analytical
      criteria (e.g., the inter-component correlation or mutual information), but do
      not consider all aspects of the coding system applied to the transformed
      components. Recently, a framework that produces optimized MCTs dependent on the
      input image and the subsequent coding system was proposed for 8-bit pathology
      whole-slide images. This work extends this framework to higher bitdepths and
      investigate its performance for different types of high-dynamic range (HDR)
      contents. Experimental results indicate that the optimized MCTs yield average
      PSNR results 0.17%, 0.47% and 0.63% higher than those of the KLT for raw mosaic
      images, reconstructed HDR radiance scenes and color graded HDR contents,
      respectively.}
      }
      
      
      
    • Miguel Hernández-Cabronero, Ian Blanes, Armando J. Pinho, Michael W. Marcellin, Joan Serra-Sagristà,
      "Progressive Lossy-to-Lossless Compression of DNA Microarray Images",
      IEEE Signal Processing Letters, vol.23, no.5, pp.698-702, May 2016.


      (Also available at the publisher's site)

      The analysis techniques applied to DNA microarray images are under active development. As new techniques become available, it will be useful to apply them to existing microarray images to obtain more accurate results. The compression of these images can be a useful tool to alleviate the costs associated to their storage and transmission. The recently proposed Relative Quantizer (RQ) coder provides the most competitive lossy compression ratios while introducing only acceptable changes in the images. However, images compressed with the RQ coder can only be reconstructed with a limited quality, determined before compression. In this work, a progressive lossy-to lossless scheme is presented to solve this problem. Firstly, the regular structure of the RQ intervals is exploited to define a lossy-to-lossless coding algorithm called the Progressive RQ (PRQ) coder. Secondly, an enhanced version that prioritizes a region of interest, called the PRQ-ROI coder, is described. Experiments indicate that the PRQ coder offers progressivity with lossless and lossy coding performance almost identical to the best techniques in the literature, none of which is progressive. In turn, the PRQ-ROI exhibits very similar lossless coding results with better rate-distortion performance than both the RQ and PRQ coders.
      @article { Hernandez16SPL,
      	author = {Miguel Hern{\'a}ndez-Cabronero and Ian Blanes and Armando J. Pinho
      and Michael W. Marcellin and Joan Serra-Sagrist{\`a}},
      	title = {Progressive Lossy-to-Lossless Compression of DNA Microarray Images},
      	journal = {IEEE Signal Processing Letters},
      	year = {2016},
      	month = {May},
      	number = {5},
      	pages = {698-702},
      	volume = {23},
      	url = {http://dx.doi.org/10.1109/LSP.2016.2547893},
      	abstract = {The analysis techniques applied to DNA microarray images are under
      active development. As new techniques become available, it will be useful to
      apply them to existing microarray images to obtain more accurate results. The
      compression of these images can be a useful tool to alleviate the costs
      associated to their storage and transmission. The recently proposed Relative
      Quantizer (RQ) coder provides the most competitive lossy compression ratios
      while introducing only acceptable changes in the images. However, images
      compressed with the RQ coder can only be reconstructed with a limited quality,
      determined before compression. In this work, a progressive lossy-to lossless
      scheme is presented to solve this problem. Firstly, the regular structure of the
      RQ intervals is exploited to define a lossy-to-lossless coding algorithm called
      the Progressive RQ (PRQ) coder. Secondly, an enhanced version that prioritizes a
      region of interest, called the PRQ-ROI coder, is described. Experiments indicate
      that the PRQ coder offers progressivity with lossless and lossy coding
      performance almost identical to the best techniques in the literature, none of
      which is progressive. In turn, the PRQ-ROI exhibits very similar lossless coding
      results with better rate-distortion performance than both the RQ and PRQ
      coders.}
      }
      
      
      
    • Victor Sanchez, Miguel Hernández-Cabronero, Francesc Aulí-Llinàs, Joan Serra-Sagristà,
      "Fast Lossless Compression Of Whole Slide Pathology Images Using HEVC Intra-Prediction",
      In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, March 2016.


      (Full text may be available at the publisher's site)

      The lossless compression of Whole Slide pathology Images (WSIs) using HEVC is investigated in this paper. Recently proposed intra-prediction algorithms based on differential pulse-code modulation (DPCM) and edge prediction provide significant bitrate improvements for a wide range of natural and screen content sequences, including WSIs. However, coding times remain relatively high due to the high number (35) of modes to be tested. In this paper, FastIntra, a novel method that requires testing only four modes is proposed. Among these four modes, FastIntra introduces a novel median edge predictor designed to accurately predict edges in different directionalities. Performance evaluations on various WSIs show average compression time reductions of 23.5% with important lossless coding improvements as compared to current block-wise intra-prediction and DPCM-based methods.
      @inproceedings { Hernandez16ICASSP,
      	author = {Victor Sanchez and Miguel Hern{\'a}ndez-Cabronero and Francesc
      Aul{\'i}-Llin{\`a}s and Joan Serra-Sagrist{\`a}},
      	title = {Fast Lossless Compression Of Whole Slide Pathology Images Using HEVC
      Intra-Prediction},
      	booktitle = {In Proceedings of the IEEE International Conference on Acoustics,
      Speech and Signal Processing, ICASSP},
      	year = {2016},
      	month = {March},
      	url = {http://ieeexplore.ieee.org/document/7471918/},
      	abstract = {The lossless compression of Whole Slide pathology Images (WSIs)
      using HEVC is investigated in this paper. Recently proposed intra-prediction
      algorithms based on differential pulse-code modulation (DPCM) and edge
      prediction provide significant bitrate improvements for a wide range of natural
      and screen content sequences, including WSIs. However, coding times remain
      relatively high due to the high number (35) of modes to be tested. In this
      paper, FastIntra, a novel method that requires testing only four modes is
      proposed. Among these four modes, FastIntra introduces a novel median edge
      predictor designed to accurately predict edges in different directionalities.
      Performance evaluations on various WSIs show average compression time reductions
      of 23.5% with important lossless coding improvements as compared to current
      block-wise intra-prediction and DPCM-based methods.}
      }
      
      
      
    • Miguel Hernández-Cabronero, Francesc Aulí-Llinàs, Victor Sanchez, Joan Serra-Sagristà,
      "Transform Optimization for the Lossy Coding of Pathology Whole-Slide Images",
      In proceedings of the IEEE Data Compression Conference, DCC, pp.131--140, March 2016.


      (Also available at the publisher's site)

      Whole-slide images (WSIs) are high-resolution, 2D, color digital images that are becoming valuable tools for pathologists in clinical, research and formative scenarios. However, their massive size is hindering their widespread adoption. Even though lossy compression can effectively reduce compressed file sizes without affecting subsequent diagnoses, no lossy coding scheme tailored for WSIs has been described in the literature. In this paper, a novel strategy called OptimizeMCT is proposed to increase the lossy coding performance for this type of images. In particular, an optimization method is designed to find image-specific multi-component transforms (MCTs) that exploit the high inter-component correlation present in WSIs. Experimental evidence indicates that the transforms yielded by OptimizeMCT consistently attain better coding performance than the Karhunen-Loève Transform (KLT) for all tested lymphatic, pancreatic and renal WSIs. More specifically, images reconstructed at the same bitrate exhibit average PSNR values 2.85 dB higher for OptimizeMCT than for the KLT, with differences of up to 5.17dB.
      @inproceedings { Hernandez16DCC,
      	author = {Miguel Hern{\'a}ndez-Cabronero and Francesc Aul{\'i}-Llin{\`a}s and
      Victor Sanchez and Joan Serra-Sagrist{\`a}},
      	title = {Transform Optimization for the Lossy Coding of Pathology Whole-Slide
      Images},
      	booktitle = {In proceedings of the IEEE Data Compression Conference, DCC},
      	year = {2016},
      	month = {March},
      	pages = {131--140},
      	url = {http://ieeexplore.ieee.org/document/7786157/},
      	abstract = {Whole-slide images (WSIs) are high-resolution, 2D, color digital
      images that are becoming valuable tools for pathologists in clinical, research
      and formative scenarios. However, their massive size is hindering their
      widespread adoption. Even though lossy compression can effectively reduce
      compressed file sizes without affecting subsequent diagnoses, no lossy coding
      scheme tailored for WSIs has been described in the literature. In this paper, a
      novel strategy called OptimizeMCT is proposed to increase the lossy coding
      performance for this type of images. In particular, an optimization method is
      designed to find image-specific multi-component transforms (MCTs) that exploit
      the high inter-component correlation present in WSIs. Experimental evidence
      indicates that the transforms yielded by OptimizeMCT consistently attain better
      coding performance than the Karhunen-Loève Transform (KLT) for all tested
      lymphatic, pancreatic and renal WSIs. More specifically, images reconstructed at
      the same bitrate exhibit average PSNR values 2.85 dB higher for OptimizeMCT than
      for the KLT, with differences of up to 5.17dB.}
      }
      
      
      
    • Naoufal Amrani, Joan Serra-Sagristà, Miguel Hernández-Cabronero, Michael W. Marcellin,
      "Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Data",
      In proceedings of the IEEE Data Compression Conference, DCC, pp.121-130, March 2016.


      (Full text may be available at the publisher's site)

      Regression Wavelet Analysis (RWA) is a novel wavelet-based scheme for coding hyperspectral images that employs multiple regression analysis to exploit the relationships among spectral wavelet-transformed components. The scheme is based on a pyramidal prediction, using different regression models, to increase the statistical independence in the wavelet domain. For lossless coding, RWA has proven to be superior to other spectral transform like PCA and to the best and most recent coding standard in remote sensing, CCSDS-123.0. In this paper we show that RWA also allows progressive lossy-to-lossless (PLL) coding and that it attains a rate-distortion performance superior to those obtained with state-of-the-art schemes. To take into account the predictive significance of the spectral components, we propose a Prediction Weighting scheme for JPEG2000 that captures the contribution of each transformed component to the prediction process.
      @inproceedings { Naoufal16DCC,
      	author = {Naoufal Amrani and Joan Serra-Sagrist{\`a} and Miguel
      Hern{\'a}ndez-Cabronero and Michael W. Marcellin},
      	title = {Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding
      of Remote-Sensing Data},
      	booktitle = {In proceedings of the IEEE Data Compression Conference, DCC},
      	year = {2016},
      	month = {March},
      	pages = {121-130},
      	url = {http://ieeexplore.ieee.org/document/7786156/},
      	abstract = {Regression Wavelet Analysis (RWA) is a novel wavelet-based scheme
      for coding hyperspectral images that employs multiple regression analysis to
      exploit the relationships among spectral wavelet-transformed components. The
      scheme is based on a pyramidal prediction, using different regression models, to
      increase the statistical independence in the wavelet domain. For lossless
      coding, RWA has proven to be superior to other spectral transform like PCA and
      to the best and most recent coding standard in remote sensing, CCSDS-123.0. In
      this paper we show that RWA also allows progressive lossy-to-lossless (PLL)
      coding and that it attains a rate-distortion performance superior to those
      obtained with state-of-the-art schemes. To take into account the predictive
      significance of the spectral components, we propose a Prediction Weighting
      scheme for JPEG2000 that captures the contribution of each transformed component
      to the prediction process.}
      }
      
      
      
    • Miguel Hernández-Cabronero, Ian Blanes, Armando J. Pinho, Michael W. Marcellin, Joan Serra-Sagristà,
      "Analysis-Driven Lossy Compression of DNA Microarray Images",
      IEEE Transactions on Medical Imaging, vol.35, no.2, pp.654-664, February 2016.


      (Also available at the publisher's site)

      DNA microarrays are one of the fastest-growing new technologies in the field of genetic research, and DNA microarray images continue to grow in number and size. Since analysis techniques are under active and ongoing development, storage, transmission and sharing of DNA microarray images need be addressed, with compression playing a significant role. However, existing lossless coding algorithms yield only limited compression performance (compression ratios below 2:1), whereas lossy coding methods may introduce unacceptable distortions in the analysis process. This work introduces a novel Relative Quantizer (RQ), which employs non-uniform quantization intervals designed for improved compression while bounding the impact on the DNA microarray analysis. This quantizer constrains the maximum relative error introduced into quantized imagery, devoting higher precision to pixels critical to the analysis process. For suitable parameter choices, the resulting variations in the DNA microarray analysis are less than half of those inherent to the experimental variability. Experimental results reveal that appropriate analysis can still be performed for average compression ratios exceeding 4.5:1.
      @article { Hernandez16TMI,
      	author = {Miguel Hern{\'a}ndez-Cabronero and Ian Blanes and Armando J. Pinho
      and Michael W. Marcellin and Joan Serra-Sagrist{\`a}},
      	title = {Analysis-Driven Lossy Compression of DNA Microarray Images},
      	journal = {IEEE Transactions on Medical Imaging},
      	year = {2016},
      	month = {February},
      	number = {2},
      	pages = {654-664},
      	volume = {35},
      	url = {http://dx.doi.org/10.1109/TMI.2015.2489262},
      	abstract = {DNA microarrays are one of the fastest-growing new technologies in
      the field of genetic research, and DNA microarray images continue to grow in
      number and size. Since analysis techniques are under active and ongoing
      development, storage, transmission and sharing of DNA microarray images need be
      addressed, with compression playing a significant role. However, existing
      lossless coding algorithms yield only limited compression performance
      (compression ratios below 2:1), whereas lossy coding methods may introduce
      unacceptable distortions in the analysis process. This work introduces a novel
      Relative Quantizer (RQ), which employs non-uniform quantization intervals
      designed for improved compression while bounding the impact on the DNA
      microarray analysis. This quantizer constrains the maximum relative error
      introduced into quantized imagery, devoting higher precision to pixels critical
      to the analysis process. For suitable parameter choices, the resulting
      variations in the DNA microarray analysis are less than half of those inherent
      to the experimental variability. Experimental results reveal that appropriate
      analysis can still be performed for average compression ratios exceeding 4.5:1.}
      }
      
      
      

    2015

    • Ian Blanes, Miguel Hernández-Cabronero, Francesc Aulí-Llinàs, Joan Serra-Sagristà, Michael W. Marcellin,
      "Isorange Pairwise Orthogonal Transform",
      IEEE Transactions on Geoscience and Remote Sensing, vol.53, no.6, pp.3361-3372, June 2015.


      (Full text may be available at the publisher's site)

      @article { Blanes15,
      	author = {Ian Blanes and Miguel Hern{\'a}ndez-Cabronero and Francesc
      Aul{\'i}-Llin{\`a}s and Joan Serra-Sagrist{\`a} and Michael W. Marcellin},
      	title = {Isorange Pairwise Orthogonal Transform},
      	journal = {IEEE Transactions on Geoscience and Remote Sensing},
      	year = {2015},
      	month = {June},
      	number = {6},
      	pages = {3361-3372},
      	volume = {53},
      	url = {http://dx.doi.org/10.1109/TGRS.2014.2374473},
      }
      
      
      

    2014

    • Miguel Hernández-Cabronero, Victor Sanchez, Michael W. Marcellin, Joan Serra-Sagristà,
      "Compression of DNA Microarray Images",
      In Book "Microarray Image and Data Analysis: Theory and Practice", CRC Press, pp.193-222, 2014.


      (Full text may be available at the publisher's site)

      @book { Hernandez14RuedaBook,
      	author = {Miguel Hern{\'a}ndez-Cabronero and Victor Sanchez and Michael W.
      Marcellin and Joan Serra-Sagrist{\`a}},
      	title = {Compression of DNA Microarray Images},
      	editor = {Luis Rueda},
      	publisher = {CRC Press},
      	year = {2014},
      	booktitle = {In Book "Microarray Image and Data Analysis: Theory and Practice",
      CRC Press},
      	pages = {193-222},
      	url = {http://www.crcpress.com/product/isbn/9781466586826},
      }
      
      
      

    2013

    • Miguel Hernández-Cabronero, Victor Sanchez, Michael W. Marcellin, Joan Serra-Sagristà,
      "A distortion metric for the lossy compression of DNA microarray images",
      In proceedings of the IEEE Data Compression Conference, DCC, pp.171-180, 2013.


      (Also available at the publisher's site)

      DNA microarrays are state-of-the-art tools in biological and medical research. In this work, we discuss the suitability of lossy compression for DNA microarray images and highlight the necessity for a distortion metric to assess the loss of relevant information. We also propose one possible metric that considers the basic image features employed by most DNA microarray analysis techniques. Experimental results indicate that the proposed metric can identify and differentiate important and unimportant changes in DNA microarray images
      @inproceedings { Hernandez13DCC,
      	author = {Miguel Hern{\'a}ndez-Cabronero and Victor Sanchez and Michael W.
      Marcellin and Joan Serra-Sagrist{\`a}},
      	title = {A distortion metric for the lossy compression of DNA microarray
      images},
      	booktitle = {In proceedings of the IEEE Data Compression Conference, DCC},
      	year = {2013},
      	pages = {171-180},
      	url = {http://dx.doi.org/10.1109/DCC.2013.26},
      	abstract = {DNA microarrays are state-of-the-art tools in biological and
      medical research. In this work, we discuss the suitability of lossy compression
      for DNA microarray images and highlight the necessity for a distortion metric to
      assess the loss of relevant information. We also propose one possible metric
      that considers the basic image features employed by most DNA microarray analysis
      techniques. Experimental results indicate that the proposed metric can identify
      and differentiate important and unimportant changes in DNA microarray images}
      }
      
      
      

    2012

    • Miguel Hernández-Cabronero, Juan Muñoz-Gómez, Ian Blanes, Joan Serra-Sagristà, Michael W. Marcellin,
      "DNA microarray image coding",
      In proceedings of the IEEE Data Compression Conference, DCC, pp.32-41, 2012.


      (Also available at the publisher's site)

      DNA microarrays are useful to identify the function and regulation of a large number of genes in a single experiment, even whole genomes. In this work, we analyze the relationship between DNA microarray image histograms and the compression performance of lossless JPEG2000. Also, a reversible transform based on histogram swapping is proposed. Intensive experimental results using different coding parameters are discussed. Results suggest that this transform improves previous lossless JPEG2000 results on all DNA microarray image sets.
      @inproceedings { Hernandez12DCC,
      	author = {Miguel Hern{\'a}ndez-Cabronero and Juan Mu{\~n}oz-G{\'o}mez and Ian
      Blanes and Joan Serra-Sagrist{\`a} and Michael W. Marcellin},
      	title = {DNA microarray image coding},
      	booktitle = {In proceedings of the IEEE Data Compression Conference, DCC},
      	year = {2012},
      	pages = {32-41},
      	doi = {10.1109/DCC.2012.11},
      	url = {http://dx.doi.org/10.1109/DCC.2012.11},
      	abstract = {DNA microarrays are useful to identify the function and regulation
      of a large number of genes in a single experiment, even whole genomes. In this
      work, we analyze the relationship between DNA microarray image histograms and
      the compression performance of lossless JPEG2000. Also, a reversible transform
      based on histogram swapping is proposed. Intensive experimental results using
      different coding parameters are discussed. Results suggest that this transform
      improves previous lossless JPEG2000 results on all DNA microarray image sets.}
      }
      
      
      
    • Miguel Hernández-Cabronero, Francesc Aulí-Llinàs, Joan Bartrina-Rapesta, Ian Blanes, Leandro Jiménez-Rodríguez, Michael W. Marcellin, Juan Muñoz-Gómez, Victor Sanchez, Joan Serra-Sagristà, Zhongwei Xu,
      "Multicomponent compression of DNA microarray images",
      In Proceedings of the CEDI Workshop on Multimedia Data Coding and Transmission, WMDCT, September 2012.


      (Also available at the publisher's site)

      In this work, the correlation present among pairs of DNA microarray images is analyzed using Pearson's r as a metric. A certain amount of correlation is found, especially for red/green channel image pairs, with averages over 0.75 for all benchmark sets. Based on that, the lossless multicomponent compression features of JPEG2000 have been tested on each set, considering different spectral and spatial transforms (DWT 5/3, DPCM, R-Haar and POT). Improvements of up to 0.6~bpp are obtained depending on the transform considered, and these improvements are consistent to the correlation values observed.
      @inproceedings { Hernandez12Sarteco,
      	author = {Miguel Hern{\'a}ndez-Cabronero and Francesc Aul{\'i}-Llin{\`a}s and
      Joan Bartrina-Rapesta and Ian Blanes and Leandro Jim{\'e}nez-Rodr{\'i}guez and
      Michael W. Marcellin and Juan Mu{\~n}oz-G{\'o}mez and Victor Sanchez and Joan
      Serra-Sagrist{\`a} and Zhongwei Xu},
      	title = {Multicomponent compression of DNA microarray images},
      	booktitle = {In Proceedings of the CEDI Workshop on Multimedia Data Coding and
      Transmission, WMDCT},
      	year = {2012},
      	month = {September},
      	url = {http://www.jornadassarteco.org/?page_id=171},
      	abstract = {In this work, the correlation present among pairs of DNA microarray
      images is analyzed using Pearson's r as a metric. A certain amount of
      correlation is found, especially for red/green channel image pairs, with
      averages over 0.75 for all benchmark sets. Based on that, the lossless
      multicomponent compression features of JPEG2000 have been tested on each set,
      considering different spectral and spatial transforms (DWT 5/3, DPCM, R-Haar and
      POT). Improvements of up to 0.6~bpp are obtained depending on the transform
      considered, and these improvements are consistent to the correlation values
      observed.}
      }
      
      
      
    • Miguel Hernández-Cabronero, Ian Blanes, Michael W. Marcellin, Joan Serra-Sagristà,
      "Standard and specific compression techniques for DNA microarray images",
      MDPI Algorithms, vol.4, pp.30-49, 2012.


      (Also available at the publisher's site)

      We review the state of the art in DNA microarray image compression and provide original comparisons between standard and microarray-specific compression techniques that validate and expand previous work. First, we describe the most relevant approaches published in the literature and classify them according to the stage of the typical image compression process where each approach makes its contribution, and then we summarize the compression results reported for these microarray-specific image compression schemes. In a set of experiments conducted for this paper, we obtain new results for several popular image coding techniques that include the most recent coding standards. Prediction-based schemes CALIC and JPEG-LS are the best performing standard compressors, but are improved upon by the best microarray-specific technique, Battiato's CNN-based scheme.
      @article { Hernandez11Algorithms,
      	author = {Miguel Hern{\'a}ndez-Cabronero and Ian Blanes and Michael W.
      Marcellin and Joan Serra-Sagrist{\`a}},
      	title = {Standard and specific compression techniques for DNA microarray
      images},
      	journal = {MDPI Algorithms},
      	year = {2012},
      	pages = {30-49},
      	volume = {4},
      	doi = {10.3390/a5010030},
      	url = {http://www.mdpi.com/1999-4893/5/1/30/},
      	abstract = {We review the state of the art in DNA microarray image compression
      and provide original comparisons between standard and microarray-specific
      compression techniques that validate and expand previous work. First, we
      describe the most relevant approaches published in the literature and classify
      them according to the stage of the typical image compression process where each
      approach makes its contribution, and then we summarize the compression results
      reported for these microarray-specific image compression schemes. In a set of
      experiments conducted for this paper, we obtain new results for several popular
      image coding techniques that include the most recent coding standards.
      Prediction-based schemes CALIC and JPEG-LS are the best performing standard
      compressors, but are improved upon by the best microarray-specific technique,
      Battiato's CNN-based scheme.}
      }
      
      
      

    2011

    • Miguel Hernández-Cabronero, Ian Blanes, Michael W. Marcellin, Joan Serra-Sagristà,
      "A review of DNA microarray image compression",
      In Proceedings of the International Conference on Data Compression, Communication and Processing, CCP, pp.139-147, June 2011.


      (Also available at the publisher's site)

      We review the state of the art in DNA microarray image compression. First, we describe the most relevant approaches published in the literature and classify them according to the stage of the typical image compression process where each approach makes its contribution. We then summarize the compression results reported for these microarray-specific image compression schemes. In a set of experiments conducted for this paper, we obtain results for several popular image coding techniques, including the most recent coding standards. Prediction-based schemes CALIC and JPEG-LS, and JPEG2000 using zero wavelet decomposition levels are the best performing standard compressors, but are all outperformed by the best microarray-specific technique, Battiato's CNN-based scheme.
      @inproceedings { Hernandez11CCP,
      	author = {Miguel Hern{\'a}ndez-Cabronero and Ian Blanes and Michael W.
      Marcellin and Joan Serra-Sagrist{\`a}},
      	title = {A review of DNA microarray image compression},
      	booktitle = {In Proceedings of the International Conference on Data
      Compression, Communication and Processing, CCP},
      	year = {2011},
      	month = {June},
      	pages = {139-147},
      	publisher = {IEEE},
      	doi = {10.1109/CCP.2011.21},
      	url = {http://dx.doi.org/10.1109/CCP.2011.21},
      	abstract = {We review the state of the art in DNA microarray image compression.
      First, we describe the most relevant approaches published in the literature and
      classify them according to the stage of the typical image compression process
      where each approach makes its contribution. We then summarize the compression
      results reported for these microarray-specific image compression schemes. In a
      set of experiments conducted for this paper, we obtain results for several
      popular image coding techniques, including the most recent coding standards.
      Prediction-based schemes CALIC and JPEG-LS, and JPEG2000 using zero wavelet
      decomposition levels are the best performing standard compressors, but are all
      outperformed by the best microarray-specific technique, Battiato's CNN-based
      scheme.}
      }
      
      
      


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