Information Bottleneck Driven Deep Video Compression—IBOpenDVCW
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 ana...
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Published in | Entropy (Basel, Switzerland) Vol. 26; no. 10; p. 836 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
30.09.2024
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Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 1099-4300 1099-4300 |
DOI: | 10.3390/e26100836 |