The Experts below are selected from a list of 13389 Experts worldwide ranked by ideXlab platform
Stefano Baronti - One of the best experts on this subject based on the ideXlab platform.
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Virtually Lossless Compression of scientific data: An application to astrophysical images
Proceedings of SPIE, 2005Co-Authors: Bruno Aiazzi, Luciano Alparone, Stefano Baronti, Cinzia Lastri, F. NenciniAbstract:ABSTRACT ThispaperdescribesanimageCompressionstrategypotentiallycapableofpreservingthescienticqualityofastrophysicaldata, simultaneously allowing a consistent bandwidth reduction to be achieved. Unlike strictly Lossless techniques, bywhich moderate Compression ratios are attainable, and conventional lossy techniques, in which the mean squared error ofthe decoded data is globally controlled by user, near-Lossless methods are capable to locally constrain the maximum ab-solute error,based on users requirements. An advancedLossless/near-Lossless differentialpulse code modulation(DPCM)scheme, recently introduced by the authors and relying on a causal spatial prediction , is adjusted to the specic char-acteristics of astrophysical image data (high radiometric resolution, generally low noise, etc.). The background noise ispreliminarilyestimated to drive the quantizationstage for high quality, which is the primaryconcernin most of astrophys-ical applications. Extensive experimental results of Lossless, near-Lossless, and lossy Compression of astrophysical imagesacquiredbytheHubbleSpaceTelescopeshowtheadvantagesoftheproposedmethodcomparedtostandardtechniqueslikeJPEG-LS and JPEG2000. Eventually, the rationale of virtually-Lossless Compression, that is a noise-adjusted lossles/near-Lossless Compression, is highlighted and found to be in accordance with concepts well established for the astronomerscommunity.Keywords: Astrophysical images, Consultative Committee for Space Data Systems (CCSDS), differential pulse codemodulation(DPCM), Lossless Compression, near-Lossless Compression,noise estimation, statistical context modeling.
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near Lossless Compression of 3 d optical data
IEEE Transactions on Geoscience and Remote Sensing, 2001Co-Authors: Bruno Aiazzi, Luciano Alparone, Stefano BarontiAbstract:Near-Lossless Compression yielding strictly bounded reconstruction error is proposed for high-quality Compression of remote sensing images. A classified causal differential pulse code modulation scheme is presented for optical data, either multi/hyperspectral three-dimensional (3-D) or panchromatic two-dimensional (2-D) observations. It is based on a classified linear-regression prediction, followed by context-based arithmetic coding of the outcome prediction errors and provides excellent performances, both for reversible and for irreversible (near-Lossless) Compression. Coding times are affordable thanks to fast convergence of training. Decoding is always real time. If the reconstruction errors fall within the boundaries of the noise distributions, the decoded images will be virtually Lossless even though encoding was not strictly reversible.
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near Lossless Compression of 3 d optical data
International Geoscience and Remote Sensing Symposium, 2001Co-Authors: Bruno Aiazzi, Luciano Alparone, Stefano BarontiAbstract:In this work, near-Lossless Compression yielding strictly bounded reconstruction error is proposed for high-quality Compression of remote sensing images. A classified causal DPCM scheme is presented for optical data, either multi/hyperspectral three-dimensional (3-D) or panchromatic two-dimensional (2-D) observations. It is based on a classified linear-regression prediction, followed by context-based arithmetic coding of the outcome prediction errors and provides excellent performances, both for reversible and for irreversible (near-Lossless) Compression. Coding times are affordable thanks to fast convergence of training. Decoding is always real time. If the reconstruction errors fall within the boundaries of the noise distributions, the decoded images will be virtually Lossless even though encoding was not strictly reversible.
Xiaolin Wu - One of the best experts on this subject based on the ideXlab platform.
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Lossless Compression of color mosaic images
IEEE Transactions on Image Processing, 2006Co-Authors: Ning Zhang, Xiaolin WuAbstract:Lossless Compression of color mosaic images poses a unique and interesting problem of spectral decorrelation of spatially interleaved R, G, B samples. We investigate reversible Lossless spectral-spatial transforms that can remove statistical redundancies in both spectral and spatial domains and discover that a particular wavelet decomposition scheme, called Mallat wavelet packet transform, is ideally suited to the task of decorrelating color mosaic data. We also propose a low-complexity adaptive context-based Golomb-Rice coding technique to compress the coefficients of Mallat wavelet packet transform. The Lossless Compression performance of the proposed method on color mosaic images is apparently the best so far among the existing Lossless image codecs.
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Lossless Compression of color mosaic images
International Conference on Image Processing, 2004Co-Authors: Ning Zhang, Xiaolin WuAbstract:We present a low complexity algorithm for Lossless Compression of color mosaic images generated by a Bayer CCD color filter array. This algorithm is based on an interesting use of the integer wavelet transform followed by a fast adaptive context-based Golomb-Rice coding. The Lossless Compression performance of the proposed algorithm is apparently the best reported in the literature so far for color mosaic images.
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ICIP - Lossless Compression of color mosaic images
2004 International Conference on Image Processing 2004. ICIP '04., 2004Co-Authors: Ning Zhang, Xiaolin WuAbstract:We present a low complexity algorithm for Lossless Compression of color mosaic images generated by a Bayer CCD color filter array. This algorithm is based on an interesting use of the integer wavelet transform followed by a fast adaptive context-based Golomb-Rice coding. The Lossless Compression performance of the proposed algorithm is apparently the best reported in the literature so far for color mosaic images.
Bruno Aiazzi - One of the best experts on this subject based on the ideXlab platform.
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Virtually Lossless Compression of scientific data: An application to astrophysical images
Proceedings of SPIE, 2005Co-Authors: Bruno Aiazzi, Luciano Alparone, Stefano Baronti, Cinzia Lastri, F. NenciniAbstract:ABSTRACT ThispaperdescribesanimageCompressionstrategypotentiallycapableofpreservingthescienticqualityofastrophysicaldata, simultaneously allowing a consistent bandwidth reduction to be achieved. Unlike strictly Lossless techniques, bywhich moderate Compression ratios are attainable, and conventional lossy techniques, in which the mean squared error ofthe decoded data is globally controlled by user, near-Lossless methods are capable to locally constrain the maximum ab-solute error,based on users requirements. An advancedLossless/near-Lossless differentialpulse code modulation(DPCM)scheme, recently introduced by the authors and relying on a causal spatial prediction , is adjusted to the specic char-acteristics of astrophysical image data (high radiometric resolution, generally low noise, etc.). The background noise ispreliminarilyestimated to drive the quantizationstage for high quality, which is the primaryconcernin most of astrophys-ical applications. Extensive experimental results of Lossless, near-Lossless, and lossy Compression of astrophysical imagesacquiredbytheHubbleSpaceTelescopeshowtheadvantagesoftheproposedmethodcomparedtostandardtechniqueslikeJPEG-LS and JPEG2000. Eventually, the rationale of virtually-Lossless Compression, that is a noise-adjusted lossles/near-Lossless Compression, is highlighted and found to be in accordance with concepts well established for the astronomerscommunity.Keywords: Astrophysical images, Consultative Committee for Space Data Systems (CCSDS), differential pulse codemodulation(DPCM), Lossless Compression, near-Lossless Compression,noise estimation, statistical context modeling.
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near Lossless Compression of 3 d optical data
IEEE Transactions on Geoscience and Remote Sensing, 2001Co-Authors: Bruno Aiazzi, Luciano Alparone, Stefano BarontiAbstract:Near-Lossless Compression yielding strictly bounded reconstruction error is proposed for high-quality Compression of remote sensing images. A classified causal differential pulse code modulation scheme is presented for optical data, either multi/hyperspectral three-dimensional (3-D) or panchromatic two-dimensional (2-D) observations. It is based on a classified linear-regression prediction, followed by context-based arithmetic coding of the outcome prediction errors and provides excellent performances, both for reversible and for irreversible (near-Lossless) Compression. Coding times are affordable thanks to fast convergence of training. Decoding is always real time. If the reconstruction errors fall within the boundaries of the noise distributions, the decoded images will be virtually Lossless even though encoding was not strictly reversible.
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near Lossless Compression of 3 d optical data
International Geoscience and Remote Sensing Symposium, 2001Co-Authors: Bruno Aiazzi, Luciano Alparone, Stefano BarontiAbstract:In this work, near-Lossless Compression yielding strictly bounded reconstruction error is proposed for high-quality Compression of remote sensing images. A classified causal DPCM scheme is presented for optical data, either multi/hyperspectral three-dimensional (3-D) or panchromatic two-dimensional (2-D) observations. It is based on a classified linear-regression prediction, followed by context-based arithmetic coding of the outcome prediction errors and provides excellent performances, both for reversible and for irreversible (near-Lossless) Compression. Coding times are affordable thanks to fast convergence of training. Decoding is always real time. If the reconstruction errors fall within the boundaries of the noise distributions, the decoded images will be virtually Lossless even though encoding was not strictly reversible.
Ning Zhang - One of the best experts on this subject based on the ideXlab platform.
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Lossless Compression of color mosaic images
IEEE Transactions on Image Processing, 2006Co-Authors: Ning Zhang, Xiaolin WuAbstract:Lossless Compression of color mosaic images poses a unique and interesting problem of spectral decorrelation of spatially interleaved R, G, B samples. We investigate reversible Lossless spectral-spatial transforms that can remove statistical redundancies in both spectral and spatial domains and discover that a particular wavelet decomposition scheme, called Mallat wavelet packet transform, is ideally suited to the task of decorrelating color mosaic data. We also propose a low-complexity adaptive context-based Golomb-Rice coding technique to compress the coefficients of Mallat wavelet packet transform. The Lossless Compression performance of the proposed method on color mosaic images is apparently the best so far among the existing Lossless image codecs.
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Lossless Compression of color mosaic images
International Conference on Image Processing, 2004Co-Authors: Ning Zhang, Xiaolin WuAbstract:We present a low complexity algorithm for Lossless Compression of color mosaic images generated by a Bayer CCD color filter array. This algorithm is based on an interesting use of the integer wavelet transform followed by a fast adaptive context-based Golomb-Rice coding. The Lossless Compression performance of the proposed algorithm is apparently the best reported in the literature so far for color mosaic images.
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ICIP - Lossless Compression of color mosaic images
2004 International Conference on Image Processing 2004. ICIP '04., 2004Co-Authors: Ning Zhang, Xiaolin WuAbstract:We present a low complexity algorithm for Lossless Compression of color mosaic images generated by a Bayer CCD color filter array. This algorithm is based on an interesting use of the integer wavelet transform followed by a fast adaptive context-based Golomb-Rice coding. The Lossless Compression performance of the proposed algorithm is apparently the best reported in the literature so far for color mosaic images.
Zhaohui Wang - One of the best experts on this subject based on the ideXlab platform.
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ISSPIT - Residual Clustering Based Lossless Compression for Remotely Sensed Images
2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2018Co-Authors: Zhaohui WangAbstract:In the K-means algorithm, every pixel in a super-space is required to calculate Euclidean distance for clustering, so it is time-consuming computing when there are a great many class centers. Improved K-means clustering algorithm presented here could save initial clustering time by making initial division based on previous clustering results, and maintain the relationship among stable classes. Only calculating and comparing distances with neighbor centers, near to the pixel except those far away from it, accelerates clustering process with more and more classes becoming stable. Clustering Lossless Compression algorithm can efficiently eliminate the inter-spectral and intra-spectral redundancy at high convergent speed through enhancing intra-class redundancy. The multi-level clustering process can not only remove the spatial redundancy but also delete the residue redundancy, whose importance in Lossless Compression was overlooked previously, realizing a breakthrough Lossless Compression ratio at 2.882 for multi-spectral images. The comparison of the parameter analysis of the TM (Landsat Thematic Mapper) images with other Lossless Compression algorithms shows that this multilevel clustering Lossless Compression algorithm is more efficient.
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Residual Clustering Based Lossless Compression for Remotely Sensed Images
2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2018Co-Authors: Zhaohui WangAbstract:In the K-means algorithm, every pixel in a super-space is required to calculate Euclidean distance for clustering, so it is time-consuming computing when there are a great many class centers. Improved K-means clustering algorithm presented here could save initial clustering time by making initial division based on previous clustering results, and maintain the relationship among stable classes. Only calculating and comparing distances with neighbor centers, near to the pixel except those far away from it, accelerates clustering process with more and more classes becoming stable. Clustering Lossless Compression algorithm can efficiently eliminate the inter-spectral and intra-spectral redundancy at high convergent speed through enhancing intra-class redundancy. The multi-level clustering process can not only remove the spatial redundancy but also delete the residue redundancy, whose importance in Lossless Compression was overlooked previously, realizing a breakthrough Lossless Compression ratio at 2.882 for multi-spectral images. The comparison of the parameter analysis of the TM (Landsat Thematic Mapper) images with other Lossless Compression algorithms shows that this multilevel clustering Lossless Compression algorithm is more efficient.
