Conditional entropy-constrained vector quantization: high-rate theory and design algorithms

Garrido, D.P. de; Pearlman, William A.

doi:10.1109/18.391238

Cited by 4 publications

(1 citation statement)

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“…The joint distribution of X and Y of two random variables can form joint entropy, expressed by H(X, Y), and the conditional entropy H(Y|X) is expressed in [27]:…”

Section: Maximum Entropy Principlementioning

confidence: 99%

Maximum-Entropy-Model-Enabled Complexity Reduction Algorithm in Modern Video Coding Standards

2020

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Symmetry considerations play a key role in modern science, and any differentiable symmetry of the action of a physical system has a corresponding conservation law. Symmetry may be regarded as reduction of Entropy. This work focuses on reducing the computational complexity of modern video coding standards by using the maximum entropy principle. The high computational complexity of the coding unit (CU) size decision in modern video coding standards is a critical challenge for real-time applications. This problem is solved in a novel approach considering CU termination, skip, and normal decisions as three-class making problems. The maximum entropy model (MEM) is formulated to the CU size decision problem, which can optimize the conditional entropy; the improved iterative scaling (IIS) algorithm is used to solve this optimization problem. The classification features consist of the spatio-temporal information of the CU, including the rate–distortion (RD) cost, coded block flag (CBF), and depth. For the case analysis, the proposed method is based on High Efficiency Video Coding (H.265/HEVC) standards. The experimental results demonstrate that the proposed method can reduce the computational complexity of the H.265/HEVC encoder significantly. Compared with the H.265/HEVC reference model, the proposed method can reduce the average encoding time by 53.27% and 56.36% under low delay and random access configurations, while Bjontegaard Delta Bit Rates (BD-BRs) are 0.72% and 0.93% on average.

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“…The joint distribution of X and Y of two random variables can form joint entropy, expressed by H(X, Y), and the conditional entropy H(Y|X) is expressed in [27]:…”

Section: Maximum Entropy Principlementioning

confidence: 99%

Maximum-Entropy-Model-Enabled Complexity Reduction Algorithm in Modern Video Coding Standards

2020

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Advances in residual vector quantization: a review

Barnes

Rizvi²,

Nasrabadi³

1996

IEEE Trans. on Image Process.

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Advances in residual vector quantization (RVQ) are surveyed. Definitions of joint encoder optimality and joint decoder optimality are discussed. Design techniques for RVQs with large numbers of stages and generally different encoder and decoder codebooks are elaborated and extended. Fixed-rate RVQs, and variable-rate RVQs that employ entropy coding are examined. Predictive and finite state RVQs designed and integrated into neural-network based source coding structures are revisited. Successive approximation RVQs that achieve embedded and refinable coding are reviewed. A new type of successive approximation RVQ that varies the instantaneous block rate by using different numbers of stages on different blocks is introduced and applied to image waveforms, and a scalar version of the new residual quantizer is applied to image subbands in an embedded wavelet transform coding system.

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