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Exploring the rate-distortion-complexity optimization in neural image compression

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2024年 105卷

作者： Gao, Yixin Feng, Runsen Guo, Zongyu Chen, Zhibo Univ Sci & Technol China Hefei Peoples R China

Despite a short history, neural image codecs have been shown to surpass classical image codecs in terms of rate-distortion performance. However, most of them suffer from significantly longer decoding times, which hinders the practical applications of neural image codecs. This issue is especially pronounced when employing an effective yet time-consuming autoregressive context model since it would increase entropy decoding time by orders of magnitude. In this paper, unlike most previous works that pursue optimal RD performance while temporally overlooking the coding complexity, we make a systematical investigation on the rate-distortioncomplexity (RDC) optimization in neural image compression. By quantifying the decoding complexity as a factor in the optimization goal, we are now able to precisely control the RDC trade-off and then demonstrate how the rate-distortion performance of neural image codecs could adapt to various complexity demands. Going beyond the investigation of RDC optimization, a variable-complexity neural codec is designed to leverage the spatial dependencies adaptively according to industrial demands, which supports fine-grained complexity adjustment by balancing the RDC tradeoff. By implementing this scheme in a powerful base model, we demonstrate the feasibility and flexibility of RDC optimization for neural image codecs.

关键词： Neural image compression Rate-distortion-complexity optimization variable-complexity

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A variable-complexity approach to second-order reliability-based optimization

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2003年第4期25卷 237-250页

作者： Burton, SA Hajela, P Rensselaer Polytech Inst Dept Mech Aerosp & Nucl Engn Troy NY 12180 USA

This paper outlines a new methodology for second-order reliability-based optimization (RBO). A variable-complexity (VC) approach is used to implement a computationally efficient VCRBO algorithm, which reduces the number of costly second-order reliability analyses by using a lower fidelity, scaled mean-value technique during the majority of the constraint assessments. Two numerical examples are presented, which provide a comparison of several standard RBO approaches with the proposed algorithm. The examples include both Gaussian and non-Gaussian uncertainty to introduce significant nonlinearities in the limit state functions (LSFs). The design spaces and LSFs for both examples are presented, along with a discussion of the computational cost associated with the different RBO approaches.

关键词： optimization reliability-based optimization second-order reliability structural reliability variable-complexity

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