The Experts below are selected from a list of 197433 Experts worldwide ranked by ideXlab platform
Xiaolin Wu - One of the best experts on this subject based on the ideXlab platform.
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Lossy-to-lossless compression of medical volumetric data using three-dimensional integer wavelet transforms
IEEE Transactions on Medical Imaging, 2003Co-Authors: Zixiang Xiong, Xiaolin Wu, S. ChengAbstract:We study lossy-to-lossless compression of medical volumetric data using three-dimensional (3-D) integer wavelet transforms. To achieve good lossy coding performance, it is important to have transforms that are unitary. In addition to the lifting approach, we first introduce a general 3-D integer wavelet packet transform structure that allows implicit bit shifting of wavelet coefficients to approximate a 3-D unitary transformation. We then focus on Context Modeling for efficient arithmetic coding of wavelet coefficients. Two state-of-the-art 3-D wavelet video coding techniques, namely, 3-D set partitioning in hierarchical trees (Kim et al., 2000) and 3-D embedded subband coding with optimal truncation (Xu et al., 2001), are modified and applied to compression of medical volumetric data, achieving the best performance published so far in the literature-both in terms of lossy and lossless compression.
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Context based lossless interband compression extending calic
IEEE Transactions on Image Processing, 2000Co-Authors: Xiaolin Wu, Nasir MemonAbstract:This paper proposes an interband version of CALIC (Context-based, adaptive, lossless image codec) which represents one of the best performing, practical and general purpose lossless image coding techniques known today. Interband coding techniques are needed for effective compression of multispectral images like color images and remotely sensed images. It is demonstrated that CALIC's techniques of Context Modeling of DPCM errors lend themselves easily to Modeling of higher-order interband correlations that cannot be exploited by simple interband linear predictors alone. The proposed interband CALIC exploits both interband and intraband statistical redundancies, and obtains significant compression gains over its intrahand counterpart. On some types of multispectral images, interband CALIC can lead to a reduction in bit rate of more than 20% as compared to intraband CALIC. Interband CALIC only incurs a modest increase in computational cost as compared to intraband CALIC.
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wavelet image coding using trellis coded space frequency quantization
IEEE Signal Processing Letters, 1999Co-Authors: Zixiang Xiong, Xiaolin WuAbstract:The progress in wavelet image coding have brought the field into its maturity. Major developments in the process are rate-distortion (R-D) based wavelet packet transformation, zerotree quantization, subband classification and trellis-coded quantization, and sophisticated Context Modeling in entropy coding. Drawing from past experience and recent in sights, we propose a new wavelet image coding technique with trellis coded space-frequency quantization (TCSFQ). TCSFQ aims to explore space-frequency characterizations of wavelet image representations via R-D optimized zerotree pruning, trellis-coded quantization, and Context Modeling in entropy coding. Experiments indicate that the TCSFQ coder achieves twice as much compression as the baseline JPEG coder does at the same peak signal to noise ratio (PSNR), making it better than all other coders described in the literature.
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Context quantization with fisher discriminant for adaptive embedded wavelet image coding
Data Compression Conference, 1999Co-Authors: Xiaolin WuAbstract:Recent progress in Context Modeling and adaptive entropy coding of wavelet coefficients has probably been the most important catalyst for the rapidly maturing area of wavelet image compression technology. In this paper we identify statistical Context Modeling of wavelet coefficients as the determining factor of rate-distortion performance of wavelet codecs. We propose a new Context quantization algorithm for minimum conditional entropy. The algorithm is a dynamic programming process guided by Fisher's linear discriminant. It facilitates high-order Context Modeling and adaptive entropy coding of embedded wavelet bit streams, and leads to superb compression performance in both lossy and lossless cases.
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wavelet image coding using trellis coded space frequency quantization
Visual Communications and Image Processing, 1998Co-Authors: Zixiang Xiong, Xiaolin WuAbstract:Recent progresses in wavelet image coding have brought the field into its maturity. Major developments in the process are rate-distortion (R-D) based wavelet packet transformation, zerotree quantization, subband classification and trellis- coded quantization, and sophisticated Context Modeling in entropy coding. Drawing from past experience and recent insight, we propose a new wavelet image coding technique with trellis coded space-frequency quantization (TCSFQ). TCSFQ aims to explore space-frequency characterizations of wavelet image representations via R-D optimized zerotree pruning, trellis coded quantization, and Context Modeling in entropy coding. Experiments indicate that the TCSFQ coder achieves twice as much compression as the baseline JPEG coder does at the same peak signal to noise ratio (PSNR), making it better than all other coders described in the literature.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
M J Weinberger - One of the best experts on this subject based on the ideXlab platform.
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applications of universal Context Modeling to lossless compression of gray scale images
IEEE Transactions on Image Processing, 1996Co-Authors: M J Weinberger, Jorma Rissanen, R B ArpsAbstract:Inspired by theoretical results on universal Modeling, a general framework for sequential Modeling of gray-scale images is proposed and applied to lossless compression. The model is based on stochastic complexity considerations and is implemented with a tree structure. It is efficiently estimated by a modification of the universal algorithm Context. Several variants of the algorithm are described. The sequential, lossless compression schemes obtained when the Context modeler is used with an arithmetic coder are tested with a representative set of gray-scale images. The compression ratios are compared with those obtained with state-of-the-art algorithms available in the literature, with the results of the comparison consistently favoring the proposed approach.
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loco i a low complexity Context based lossless image compression algorithm
Data Compression Conference, 1996Co-Authors: M J Weinberger, Gadiel Seroussi, Guillermo SapiroAbstract:LOCO-I (low complexity lossless compression for images) is a novel lossless compression algorithm for continuous-tone images which combines the simplicity of Huffman coding with the compression potential of Context models, thus "enjoying the best of both worlds." The algorithm is based on a simple fixed Context model, which approaches the capability of the more complex universal Context Modeling techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with a collection of (Context-conditioned) Huffman codes, which is realized with an adaptive, symbol-wise, Golomb-Rice code. LOCO-I attains, in one pass, and without recourse to the higher complexity arithmetic coders, compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. In fact, LOCO-I is being considered by the ISO committee as a replacement for the current lossless standard in low-complexity applications.
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applications of universal Context Modeling to lossless compression of gray scale images
Asilomar Conference on Signals Systems and Computers, 1995Co-Authors: M J Weinberger, Jorma Rissanen, R B ArpsAbstract:Inspired by theoretical results on universal Modeling, a general framework for sequential Modeling of gray-scale images is proposed and applied to lossless compression. The model is based on stochastic complexity considerations and is implemented with a tree structure. It is efficiently estimated by a modification of the universal algorithm Context. The sequential, lossless compression schemes obtained when the Context modeler is used with an arithmetic coder, are tested with a representative set of gray-scale images. The compression ratios are compared with those obtained with state-of-the-art algorithms available in the literature, with the results of the comparison, showing the potential of the proposed approach.
Zixiang Xiong - One of the best experts on this subject based on the ideXlab platform.
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Lossy-to-lossless compression of medical volumetric data using three-dimensional integer wavelet transforms
IEEE Transactions on Medical Imaging, 2003Co-Authors: Zixiang Xiong, Xiaolin Wu, S. ChengAbstract:We study lossy-to-lossless compression of medical volumetric data using three-dimensional (3-D) integer wavelet transforms. To achieve good lossy coding performance, it is important to have transforms that are unitary. In addition to the lifting approach, we first introduce a general 3-D integer wavelet packet transform structure that allows implicit bit shifting of wavelet coefficients to approximate a 3-D unitary transformation. We then focus on Context Modeling for efficient arithmetic coding of wavelet coefficients. Two state-of-the-art 3-D wavelet video coding techniques, namely, 3-D set partitioning in hierarchical trees (Kim et al., 2000) and 3-D embedded subband coding with optimal truncation (Xu et al., 2001), are modified and applied to compression of medical volumetric data, achieving the best performance published so far in the literature-both in terms of lossy and lossless compression.
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wavelet image coding using trellis coded space frequency quantization
IEEE Signal Processing Letters, 1999Co-Authors: Zixiang Xiong, Xiaolin WuAbstract:The progress in wavelet image coding have brought the field into its maturity. Major developments in the process are rate-distortion (R-D) based wavelet packet transformation, zerotree quantization, subband classification and trellis-coded quantization, and sophisticated Context Modeling in entropy coding. Drawing from past experience and recent in sights, we propose a new wavelet image coding technique with trellis coded space-frequency quantization (TCSFQ). TCSFQ aims to explore space-frequency characterizations of wavelet image representations via R-D optimized zerotree pruning, trellis-coded quantization, and Context Modeling in entropy coding. Experiments indicate that the TCSFQ coder achieves twice as much compression as the baseline JPEG coder does at the same peak signal to noise ratio (PSNR), making it better than all other coders described in the literature.
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wavelet image coding using trellis coded space frequency quantization
Visual Communications and Image Processing, 1998Co-Authors: Zixiang Xiong, Xiaolin WuAbstract:Recent progresses in wavelet image coding have brought the field into its maturity. Major developments in the process are rate-distortion (R-D) based wavelet packet transformation, zerotree quantization, subband classification and trellis- coded quantization, and sophisticated Context Modeling in entropy coding. Drawing from past experience and recent insight, we propose a new wavelet image coding technique with trellis coded space-frequency quantization (TCSFQ). TCSFQ aims to explore space-frequency characterizations of wavelet image representations via R-D optimized zerotree pruning, trellis coded quantization, and Context Modeling in entropy coding. Experiments indicate that the TCSFQ coder achieves twice as much compression as the baseline JPEG coder does at the same peak signal to noise ratio (PSNR), making it better than all other coders described in the literature.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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progressive coding of medical volumetric data using three dimensional integer wavelet packet transform
Visual Communications and Image Processing, 1998Co-Authors: Zixiang Xiong, Xiaolin Wu, W.a. PearlmanAbstract:We examine progressive lossy to lossless compression of medical volumetric data using three-dimensional (3D) integer wavelet packet transforms and set partitioning in hierarchical trees (SPIHT). To achieve good lossy coding performance, we describe a 3D integer wavelet packet transform that allows implicit bit shifting of wavelet coefficients to approximate a 3D unitary transformation. We also address Context Modeling for efficient entropy coding within the SPIHT framework. Both lossy and lossless coding performances are better than those reported recently in reference one.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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Progressive coding of medical volumetric data using three-dimensional integer wavelet packet transform
1998 IEEE Second Workshop on Multimedia Signal Processing (Cat. No.98EX175), 1998Co-Authors: Zixiang Xiong, Xiaolin Wu, W.a. PearlmanAbstract:We examine progressive lossy to lossless compression of medical volumetric data using three-dimensional (3D) integer wavelet packet transforms and set partitioning in hierarchical trees (SPIHT). To achieve good lossy coding performance, we describe a 3D integer wavelet packet transform that allows implicit bit shifting of wavelet coefficients to approximate a 3D unitary transformation. We also address Context Modeling for efficient entropy coding within the SPIHT framework. Both lossy and lossless coding performance are better than those previously reported.
Martin Vetterli - One of the best experts on this subject based on the ideXlab platform.
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spatially adaptive wavelet thresholding with Context Modeling for image denoising
IEEE Transactions on Image Processing, 2000Co-Authors: S G Chang, Bin Yu, Martin VetterliAbstract:The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the literature has focused on developing the best uniform threshold or best basis selection. However, not much has been done to make the threshold values adaptive to the spatially changing statistics of images. Such adaptivity can improve the wavelet thresholding performance because it allows additional local information of the image (such as the identification of smooth or edge regions) to be incorporated into the algorithm. This work proposes a spatially adaptive wavelet thresholding method based on Context Modeling, a common technique used in image compression to adapt the coder to changing image characteristics. Each wavelet coefficient is modeled as a random variable of a generalized Gaussian distribution with an unknown parameter. Context Modeling is used to estimate the parameter for each coefficient, which is then used to adapt the thresholding strategy. This spatially adaptive thresholding is extended to the overcomplete wavelet expansion, which yields better results than the orthogonal transform. Experimental results show that spatially adaptive wavelet thresholding yields significantly superior image quality and lower MSE than the best uniform thresholding with the original image assumed known.
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spatially adaptive wavelet thresholding with Context Modeling for image denoising
International Conference on Image Processing, 1998Co-Authors: S G Chang, Bin Yu, Martin VetterliAbstract:The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the work has been concentrated on finding the best uniform threshold or best basis. However, not much has been done to make this method adaptive to spatially changing statistics which is typical of a large class of images. This work proposes a spatially adaptive wavelet thresholding method based on Context Modeling, a common technique used in image compression to adapt the coder to the non-stationarity of images. We model each coefficient as a random variable with the generalized Gaussian prior with unknown parameters. Context Modeling is used to estimate the parameters for each coefficient, which are then used to adapt the thresholding strategy. Experimental results show that spatially adaptive wavelet thresholding yields significantly superior image quality and lower MSE than optimal uniform thresholding.
R B Arps - One of the best experts on this subject based on the ideXlab platform.
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applications of universal Context Modeling to lossless compression of gray scale images
IEEE Transactions on Image Processing, 1996Co-Authors: M J Weinberger, Jorma Rissanen, R B ArpsAbstract:Inspired by theoretical results on universal Modeling, a general framework for sequential Modeling of gray-scale images is proposed and applied to lossless compression. The model is based on stochastic complexity considerations and is implemented with a tree structure. It is efficiently estimated by a modification of the universal algorithm Context. Several variants of the algorithm are described. The sequential, lossless compression schemes obtained when the Context modeler is used with an arithmetic coder are tested with a representative set of gray-scale images. The compression ratios are compared with those obtained with state-of-the-art algorithms available in the literature, with the results of the comparison consistently favoring the proposed approach.
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applications of universal Context Modeling to lossless compression of gray scale images
Asilomar Conference on Signals Systems and Computers, 1995Co-Authors: M J Weinberger, Jorma Rissanen, R B ArpsAbstract:Inspired by theoretical results on universal Modeling, a general framework for sequential Modeling of gray-scale images is proposed and applied to lossless compression. The model is based on stochastic complexity considerations and is implemented with a tree structure. It is efficiently estimated by a modification of the universal algorithm Context. The sequential, lossless compression schemes obtained when the Context modeler is used with an arithmetic coder, are tested with a representative set of gray-scale images. The compression ratios are compared with those obtained with state-of-the-art algorithms available in the literature, with the results of the comparison, showing the potential of the proposed approach.