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Near lossless ROI compression for medical images
Optics and Precision Engineering, 2013A near lossless Region of Interest(ROI)compression algorithm based on the shearlet transform was proposed for medical images to improve the Mean Structural SIMilarity(MSSIM) between the original image and the reconstructed image.Firstly,the ROI was designated in a medical image and the rests were regard as the Background(BG).Then,the ROI and BG were ...
蒋慧琴 JIANG Hui-qin +3 more
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Near-lossless image compression: minimum-entropy, constrained-error DPCM
IEEE Transactions on Image Processing, 1998A near-lossless image compression scheme is presented. It is essentially a differential pulse code modulation (DPCM) system with a mechanism incorporated to minimize the entropy of the quantized prediction error sequence. With a "near-lossless" criterion of no more than a d gray-level error for each pixel, where d is a small nonnegative integer ...
Ke, Ligang, Marcellin, Michael W.
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Fast-adaptive near-lossless image compression
Journal of Electronic Imaging, 2016The purpose of image compression is to store or transmit image data efficiently. However, most compression methods emphasize the compression ratio rather than the throughput. We propose an encoding process and rules, and consequently a fast-adaptive near-lossless image compression method (FAIC) with good compression ratio. FAIC is a single-pass method,
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Near-Lossless Compression of Mass Spectra for Proteomics
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007Recent improvements in mass spectrometry (MS) technology led to an explosive amount of MS data collected and shared. A typical liquid chromatography/mass spectrometry (LC/MS) "image" from the instrument used in this study consists of 4 GB of data.
Agnieszka C. Miguel +5 more
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Near-lossless bandwidth compression for radiometric data
Optical Engineering, 1991We present a bandwidth compression scheme suitable for transmission of radiometric data collected bytoday's sensitive and high-resolution sensors. Specific design constraints associated with this application are requirements for (1) near-lossless coding, (2) handling of a high dynamic range, and (3) placement of an upper bound on maximum coding error ...
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Single-rate near lossless compression of animated geometry
Computer-Aided Design, 2009Representing mesh geometry in local rather than the world coordinate systems is very useful in many 3D animation processing applications. One can investigate the representation of vertex locations relative to a local coordinate frame (LCF) in the compression of dynamic 3D meshes.
Rachida Amjoun, Wolfgang Straßer
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Near lossless data compression onboard a hyperspectral satellite
IEEE Transactions on Aerospace and Electronic Systems, 2006To deal with the large volume of data produced by hyperspectral sensors, the Canadian Space Agency (CSA) has developed and patented two near lossless data compression algorithms for use onboard a hyperspectral satellite: successive approximation multi-stage vector quantization (SAMVQ) and hierarchical self-organizing cluster vector quantization (HSOCVQ)
Shen-en Qian +4 more
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A progressive Lossless/Near-Lossless image compression algorithm
IEEE Signal Processing Letters, 2002A novel image compression technique is presented that incorporates progressive transmission and near-lossless compression in a single framework. Experimental performance of the proposed coder proves to be competitive with the state-of-the-art compression schemes.
I. Avcibas +3 more
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Distributed near lossless compression algorithm for hyperspectral images
Computers & Electrical Engineering, 2014Abstract This paper presents a new near lossless compression algorithm for hyperspectral images based on distributed source coding. The algorithm is performed on blocks that have the same location and size in each band. Because the importance varies from block to block along the spectral orientation, an adaptive rate allocation algorithm that weights
Yongjian Nian, Mi He, Jianwei Wan
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Onboard Near-Lossless Data Compression Techniques
2013The vector-quantization-based lossy compression algorithms discussed in Chapter 4 can easily achieve compression ratios of 50:1 or more if some loss in fidelity of the reconstructed data can be tolerated in exchange for the higher compression ratio. Caution must be taken when a lossy data compression algorithm is applied to satellite data. For example,
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