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Article

Information Bottleneck Driven Deep Video Compression—IBOpenDVCW

by
Timor Leiderman
* and
Yosef Ben Ezra
*
Faculty of Electrical Engineering, Holon Institute of Technology, 52 Golomb Str., P.O. Box 305, Holon 58102, Israel
*
Authors to whom correspondence should be addressed.
Entropy 2024, 26(10), 836; https://doi.org/10.3390/e26100836
Submission received: 14 August 2024 / Revised: 26 September 2024 / Accepted: 29 September 2024 / Published: 30 September 2024
(This article belongs to the Section Information Theory, Probability and Statistics)

Abstract

Video compression remains a challenging task despite significant advancements in end-to-end optimized deep networks for video coding. This study, inspired by information bottleneck (IB) theory, introduces a novel approach that combines IB theory with wavelet transform. We perform a comprehensive analysis of information and mutual information across various mother wavelets and decomposition levels. Additionally, we replace the conventional average pooling layers with a discrete wavelet transform creating more advanced pooling methods to investigate their effects on information and mutual information. Our results demonstrate that the proposed model and training technique outperform existing state-of-the-art video compression methods, delivering competitive rate-distortion performance compared to the AVC/H.264 and HEVC/H.265 codecs.
Keywords: deep video compression; wavelets; information bottleneck; neural networks deep video compression; wavelets; information bottleneck; neural networks

Share and Cite

MDPI and ACS Style

Leiderman, T.; Ben Ezra, Y. Information Bottleneck Driven Deep Video Compression—IBOpenDVCW. Entropy 2024, 26, 836. https://doi.org/10.3390/e26100836

AMA Style

Leiderman T, Ben Ezra Y. Information Bottleneck Driven Deep Video Compression—IBOpenDVCW. Entropy. 2024; 26(10):836. https://doi.org/10.3390/e26100836

Chicago/Turabian Style

Leiderman, Timor, and Yosef Ben Ezra. 2024. "Information Bottleneck Driven Deep Video Compression—IBOpenDVCW" Entropy 26, no. 10: 836. https://doi.org/10.3390/e26100836

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