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Design of Optimal entropy-constrained Unrestricted Polar quantizer for Bivariate Circularly Symmetric Sources

IEEE TRANSACTIONS ON COMMUNICATIONS 2018年第5期66卷 2169-2180页

作者： Wu, Huihui Dumitrescu, Sorina McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada

This paper proposes an algorithm for the design of entropy-constrained unrestricted polar quantizer (ECUPQ) for bivariate circularly symmetric sources. The algorithm is globally optimal for the class of ECUPQs with magnitude quantizer thresholds confined to a finite set. The optimization problem is formulated as the minimization of a weighted sum of distortion and entropy, and the proposed solution is based on modeling the problem as a minimum-weight path problem in a certain weighted directed acyclic graph. Each graph edge corresponds to a possible magnitude quantizer bin and computing its weight involves solving another optimization problem. We develop a fast strategy for evaluating all edge weights, leading to a O(K-2 + KPmax) time solution algorithm, where K is the size of the set of possible magnitude thresholds and P-max is the maximum number of phase levels. The practical performance of the proposed algorithm is assessed for a bivariate circularly symmetric Gaussian source, at rates ranging from 0.5 to 6 bits/sample. Our results demonstrate that the proposed approach achieves performance very close to the asymptotically optimal ECUPQ at all rates, while at low rates it significantly outperforms all previous UPQ schemes. Notably, peak improvement of 0.755 dB can be achieved for rates below 2.5.

关键词： Unrestricted polar quantization entropy-constrained quantizer globally optimal algorithm minimum-weight path problem

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DESIGN OF OPTIMAL entropy-constrained UNRESTRICTED POLAR quantizer FOR BIVARIATE CIRCULARLY SYMMETRIC SOURCES

DESIGN OF OPTIMAL ENTROPY-CONSTRAINED UNRESTRICTED POLAR QUA...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Wu, Huihui Dumitrescu, Sorina McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada

ISBN: (纸本)9781538646588

关键词： Unrestricted polar quantization globally optimal solution entropy-constrained quantizer minimum-weight path problem

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APPROACHING OPTIMALITY IN SPATIALLY SCALABLE VIDEO CODING: FROM RESAMPLING AND PREDICTION TO QUANTIZATION AND entropy CODING

APPROACHING OPTIMALITY IN SPATIALLY SCALABLE VIDEO CODING: F...

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20th IEEE International Conference on Image Processing (ICIP)

作者： Han, Jingning Rose, Kenneth Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

ISBN: (纸本)9781479923410

This paper builds on our recent work on optimal prediction in spatially scalable video coding, and is inspired by earlier work in our lab on optimal approaches for quality (or SNR) scalability. The approach we propose herein complements the optimal enhancementlayer prediction, enabled by transform domain resampling that ensures the base layer information is maximally accessible and usable at the enhancement layer despite their differing signal resolutions, with an optimal approach to quantization and entropy coding that exploits all available information, encapsulated in the appropriate conditional distribution for transform coefficients, to yield a unified coding engine for spatial scalability. For such quantizers to fully exploit base layer information, the enhancement layer transform block size must proportionally match the signal block transformed at the base layer. The overall system incorporates switching that applies the full estimation-theoretic quantizer and entropy coder at the right block size, but may optionally employ other block sizes where it defaults to optimal prediction followed by standard quantization. It is experimentally shown that the proposed scheme provides considerable performance gains over conventional codec and other leading competitors.

关键词： Scalable video coding spatial scalability estimation theory entropy-constrained quantizer arithmetic coding

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APPROACHING OPTIMALITY IN SPATIALLY SCALABLE VIDEO CODING: FROM RESAMPLING AND PREDICTION TO QUANTIZATION AND entropy CODING

APPROACHING OPTIMALITY IN SPATIALLY SCALABLE VIDEO CODING: F...

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IEEE International Conference on Image Processing

作者： Jingning Han Kenneth Rose Department of Electrical and Computer Engineering University of California Santa Barbara CA 93106

ISBN: (纸本)9781479923427

This paper builds on our recent work on optimal prediction in spatially scalable video coding, and is inspired by earlier work in our lab on optimal approaches for quality (or SNR) scalability. The approach we propose herein complements the optimal enhancement-layer prediction, enabled by transform domain resampling that ensures the base layer information is maximally accessible and usable at the enhancement layer despite their differing signal resolutions, with an optimal approach to quantization and entropy coding that exploits all available information, encapsulated in the appropriate conditional distribution for transform coefficients, to yield a unified coding engine for spatial scalability. For such quantizers to fully exploit base layer information, the enhancement layer transform block size must proportionally match the signal block transformed at the base layer. The overall system incorporates switching that applies the full estimation-theoretic quantizer and entropy coder at the right block size, but may optionally employ other block sizes where it defaults to optimal prediction followed by standard quantization. It is experimentally shown that the proposed scheme provides considerable performance gains over conventional codec and other leading competitors.

关键词： Scalable video coding spatial scalability estimation theory entropy-constrained quantizer arithmetic coding

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