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IEEE International Conference on Image Processing

作者： M.A.U. Khan W.A.H. Mousa Department of Electrical Engineering King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

Residual vector quantization (RVQ) is a structurally constrained vector quantization (VQ) paradigm. RVQ employs multipath search and has higher encoding cost as compared to sequential single-path search. Reflected residual vector quantization (Ref-RVQ), a design with additional symmetry on the codebook, was developed later to a jointly optimized RVQ structure with single-path search. The constrained Ref-RVQ codebook exhibits an increase in distortion. However, it was conjectured that the Ref-RVQ codebook has a lower output entropy than that of the multipath RVQ codebook. Therefore, the Ref-RVQ design was generalized to include noiseless entropy coding. We apply it to image coding. The method is referred to as entropy-constrained Ref-RVQ (EC-Ref-RVQ). Since the RVQ scheme is able to implement very large dimensional vector quantization designs like 16/spl times/16 and 32/spl times/32 VQs, it is found highly successful in extracting linear and non-linear correlation among image pixels. We intend to implement these large dimensional vectors with the EC-Ref-RVQ scheme to realize a computationally less demanding image-RVQ design. Simulation results demonstrate that EC-Ref-RVQ, while maintaining single path search, provides 1 dB improvement in PSNR for image data over the multipath EC-RVQ.

关键词： Image coding Vector quantization Encoding Costs Design optimization Nonlinear distortion entropy coding Data mining Pixel Computational modeling

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Joint space-frequency segmentation, entropy coding and the compression of ultrasound images

Joint space-frequency segmentation, entropy coding and the c...

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IEEE International Conference on Image Processing

作者： E. Chiu J. Vaisey S. Atkins School of Engineering Science Simon Fraser University Burnaby BC Canada School of Computing Science Simon Fraser University Burnaby BC Canada

Joint space-frequency segmentation is a relatively new image compression technique that finds the rate-distortion optimal representation of an image from a large set of possible space-frequency partitions and quantizer combinations. As such, the method is especially effective when the images to code are statistically inhomogeneous, which is certainly the case in the ultrasound modality. Unfortunately, however, the original paper on space-frequency segmentation neglected to use an actual entropy coder, but instead relied upon the zeroth-order entropy to guide the algorithm. In this work, we fill this gap by comparing actual entropy-coding strategies and their effect on both the resulting segmentations as well as the rate-distortion performance. We then apply the resulting "complete" algorithm to representative ultrasound images. The result is an effective technique that performs significantly better than SPIHT using both objective and subjective measures.

关键词： Image segmentation entropy coding Image coding Ultrasonic imaging Frequency Wavelet packets Partitioning algorithms Rate-distortion Ultrasonic variables measurement Wavelet transforms

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Multi-scale zerotree entropy coding

Multi-scale zerotree entropy coding

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： I. Sodagar Hung-Ju Lee P. Hatrack Bing-Bing Chai Sarnoff Corporation Princeton NJ USA

This paper describes the visual texture compression scheme adopted for MPEG-4 international standard. The scheme is based on the concept of multiscale zerotree wavelet entropy coding (MZTE) technique that provides different levels of scalability layer in terms of either spatial resolutions or picture quality. MZTE provides much improved compression efficiency and fine-gradual scalabilities. The MZTE scheme is adopted as the baseline technique for the visual texture coding profile in both MPEG4 video group and SNHC group.

关键词： entropy coding Scalability Decoding Image coding Spatial resolution Image segmentation MPEG 4 Standard Image resolution Video compression Wavelet transforms

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Lookahead search for lossy context-based adaptive entropy coding

Lookahead search for lossy context-based adaptive entropy co...

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IEEE International Conference on Image Processing

作者： R. Singh A. Ortega Integrated Media Systems Center Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA

We motivate the need for lookahead search in a context-based entropy coder. An efficient algorithm based on modeling of the context coder as a finite state machine is presented. A key contribution of this paper is the use of the per survivor processing (PSP) principle to enable a lookahead search in scenarios where adaptive entropy coding is used. Our results show that lookahead searches based on PSP result in performance improvements over traditional schemes.

关键词： entropy coding Quantization Context modeling Image coding Cost function Rate distortion theory Automata Arithmetic Code standards Data engineering

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Robust wireless image transmission based on turbo-coded OFDM

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2002年第3期48卷 724-731页

作者： Yang, J Lee, MH Jiang, MQ Park, JY Wuhan Univ Technol Wuhan 430070 Peoples R China Chonbuk Natl Univ Inst Inform & Comm Chonju South Korea Wuhan Univ Wuhan 430072 Peoples R China

In this paper a robust image transmission system incorporating perceptually-based coding and error protection is proposed for wireless channels. The perceptually-based image compression coder consists of a wavelet transform, adaptive quantization and variable-length entropy encoding. Uniform quantization, adaptive quantization were all computed for the wavelet transform and comparisons of the methods are presented The performance of adaptive quantization is superior to that of uniform quantization The quantization data are encoded using Turbo code. It is shown, through simulations, that wireless image transmission using turbo coder OFDM is better suited to a multipath channel than a, single carrier transmission technique.

关键词： OFDM modulation modulation coding turbo codes visual communication image coding transform coding quantisation (signal) discrete wavelet transforms multipath channels adaptive signal processing variable length codes entropy codes turbo-cod Orthogonal frequency division multiplexing Multipath channels transform coding uniform quantization adaptive signal processing Image communication systems image coding entropy coding adaptive quantization turbo codes discrete wavelet transform modulation coding variable length codes

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On the structure of optimal entropy-constrained scalar quantizers

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IEEE TRANSACTIONS ON INFORMATION THEORY 2002年第2期48卷 416-427页

作者： György, A Linder, T Budapest Univ Technol & Econ Dept Comp Sci & Informat Theory H-1521 Budapest Hungary Queens Univ Dept Math & Stat Kingston ON K7L 3N6 Canada

The nearest neighbor condition implies that when searching for a mean-square optimal fixed-rate quantizer it is enough to consider the class of regular quantizers, i.e., quantizers having convex cells and codepoints which lie inside the associated cells. In contrast, quantizer regularity can preclude optimality in entropy-constrained quantization. This can be seen by exhibiting a simple discrete scalar source for which the mean-square optimal entropy-constrained scalar quantizer (ECSQ) has disconnected (and hence nonconvex) cells at certain rates. In this work, new results concerning the structure and existence of optimal ECSQs are presented. One main result shows that for continuous sources and distortion measures of the form d(x, y) = rho(\x - y\), where rho is a nondecreasing convex function, any finite-level ECSQ can be "regularized" so that the resulting regular quantizer has the same entropy and equal or less distortion. Regarding the existence of optimal ECSQs, we prove that under rather general conditions there exists an "almost regular" optimal ECSQ for any entropy constraint. For the squared error distortion measure and sources with piecewise-monotone and continuous densities, the existence of a regular optimal ECSQ is shown.

关键词： convex distortion measures entropy coding optimal quantization regular quantizers

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Successive refinement lattice vector quantization

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2002年第12期11卷 1337-1348页

作者： Mukherjee, D Mitra, SK Univ Calif Santa Barbara Image Proc Lab Dept Elect & Comp Engn Santa Barbara CA 93106 USA

Lattice Vector Quantization (LVQ) solves the complexity problem of LBG based vector quantizers, yielding very general codebooks. However, a single stage LVQ, when applied to high resolution quantization of a vector, may result in very large and unwieldy indices, making it unsuitable for applications requiring successive refinement. The goal of this work is to develop a unified framework for progressive uniform quantization of vectors without having to sacrifice the mean- squared-error advantage of lattice quantization. A successive refinement uniform vector quantization methodology is developed, where the codebooks in successive stages are all lattice codebooks, each in the shape of the Voronoi regions of the lattice at the previous stage. Such Voronoi shaped geometric lattice codebooks are named Voronoi lattice VQs (VLVQ). Measures of efficiency of successive refinement are developed based on the entropy of the indices transmitted by the VLVQs. Additionally, a constructive method for asymptotically optimal uniform quantization is developed using tree-structured subset VLVQs in conjunction with entropy coding. The methodology developed here essentially yields the optimal vector counterpart of scalar "bitplane-wise" refinement. Unfortunately it is not as trivial to implement as in the scalar case. Furthermore, the benefits of asymptotic optimality in tree-structured subset VLVQs remain elusive in practical nonasymptotic situations. Nevertheless, because scalar bitplane- wise refinement is extensively used in modern wavelet image coders, we have applied the VLVQ techniques to successively refine vectors of wavelet coefficients in the vector set-partitioning (VSPIHT) framework. The results are compared against SPHIT and the previous Successive Approximation Wavelet Vector Quantization (SA-W-VQ) results of Sampson, da Silva, and Ghanbari.

关键词： entropy coding lattice vector quantization vector SPIHT Voronoi region wavelets zerotree

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Context modeling for near-lossless image coding

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IEEE SIGNAL PROCESSING LETTERS 2002年第3期9卷 77-80页

作者： Aiazzi, B Alparone, L Baronti, S CNR IROE Nello Carrara Res Inst Electromagnet Waves I-50127 Florence Italy Univ Florence Dept Elect & Telecommun I-50139 Florence Italy

This letter describes a context-based entropy coding suitable for any causal spatial differential pulse code modulation (DPCM) scheme performing lossless or near-lossless image coding. The proposed method is based on partitioning of prediction errors into homogeneous classes before arithmetic coding. A context function is measured on prediction errors lying within a two-dimensional (2-D) causal neighborhood, comprising the prediction support of the current pixel, as the root mean square (RMS) of residuals weighted by the reciprocal of their Euclidean distances. Its effectiveness is demonstrated in comparative experiments concerning both lossless and near-lossless coding. The proposed context coding/decoding is strictly real-time.

关键词： arithmetic coding DPCM entropy coding near-lossless coding statistical context modeling

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entropy-constrained predictive compression of SAR raw data

Entropy-constrained predictive compression of SAR raw data

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Conference on Mathematics of Data/Image coding, Compression and Encryption V, with Applications

作者： Magli, E Olmo, G Politecn Torino Dipartimento Elettron I-10129 Turin Italy

ISBN: (纸本)0819445606

In this paper we propose to employ entropy-constrained predictive coding for lossy compression of SAR raw data. We exploit the known result that a blockwise normalized SAR raw signal is a Gaussian stationary process in order to design an optimal decorrelator for this signal. The proposed predictive coding algorithm performs entropy-constrained quantization of the prediction error, followed by entropy coding;the algorithm exhibits a number of advantages, and notably a very high performance gain, with respect to other techniques such as FBAQ or methods based on transform coding. Simulation results on real-world SIR-C/X-SAR as well as simulated raw and image data show that the proposed algorithm significantly outperforms FBAQ as to SNR, at a computational cost compatible with modem SAR systems.

关键词： SAR raw data compression DPCM entropy coding entropy-constrained quantization

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Compressed domain indexing of losslessly compressed images

Compressed domain indexing of losslessly compressed images

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Storage and Retrieval for Media Databases 2002 Conference

作者： Schaefer, G Nottingham Trent Univ Dept Comp Nottingham England

ISBN: (纸本)0819444162

Image retrieval and image compression have been pursued separately in the past. Only little research has been done on a synthesis of the two by allowing image retrieval to be performed directly in the compressed domain of images without the need to uncompress them first. In this paper methods for image retrieval in the compressed domain of losslessly compressed images are introduced. While most image compression techniques are lossy, i.e. discard visually less significant information, lossless techniques are still required in fields like medical imaging or in situations where images must not be changed due to legal reasons. The algorithms in this paper are based on predictive coding methods where a pixel is encoded based on the pixel values of its (already encoded) neighbourhood. The first method is based on an understanding that predictively coded data is itself indexable and represents a textural description of the image. The second method operates directly on the entropy encoded data by comparing codebooks of images. Experiments show good image retrieval results for both approaches.

关键词： image retrieval compressed domain image retrieval midstream content access lossless image compression entropy coding

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