Channel-Wise Autoregressive Entropy Models for Learned Image Compression
In learning-based approaches to image compression, codecs are developed by optimizing a computational model to minimize a rate-distortion objective. Currently, the most effective learned image codecs take the form of an entropy-constrained autoencoder with an entropy model that uses both forward and...
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| Vydáno v: | Proceedings - International Conference on Image Processing s. 3339 - 3343 |
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| Jazyk: | angličtina |
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IEEE
01.10.2020
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| ISSN: | 2381-8549 |
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| Abstract | In learning-based approaches to image compression, codecs are developed by optimizing a computational model to minimize a rate-distortion objective. Currently, the most effective learned image codecs take the form of an entropy-constrained autoencoder with an entropy model that uses both forward and backward adaptation. Forward adaptation makes use of side information and can be efficiently integrated into a deep neural network. In contrast, backward adaptation typically makes predictions based on the causal context of each symbol, which requires serial processing that prevents efficient GPU / TPU utilization. We introduce two enhancements, channel-conditioning and latent residual prediction, that lead to network architectures with better rate-distortion performance than existing context-adaptive models while minimizing serial processing. Empirically, we see an average rate savings of 6.7% on the Kodak image set and 11.4% on the Tecnick image set compared to a context-adaptive baseline model. At low bit rates, where the improvements are most effective, our model saves up to 18% over the baseline and outperforms hand-engineered codecs like BPG by up to 25%. |
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| AbstractList | In learning-based approaches to image compression, codecs are developed by optimizing a computational model to minimize a rate-distortion objective. Currently, the most effective learned image codecs take the form of an entropy-constrained autoencoder with an entropy model that uses both forward and backward adaptation. Forward adaptation makes use of side information and can be efficiently integrated into a deep neural network. In contrast, backward adaptation typically makes predictions based on the causal context of each symbol, which requires serial processing that prevents efficient GPU / TPU utilization. We introduce two enhancements, channel-conditioning and latent residual prediction, that lead to network architectures with better rate-distortion performance than existing context-adaptive models while minimizing serial processing. Empirically, we see an average rate savings of 6.7% on the Kodak image set and 11.4% on the Tecnick image set compared to a context-adaptive baseline model. At low bit rates, where the improvements are most effective, our model saves up to 18% over the baseline and outperforms hand-engineered codecs like BPG by up to 25%. |
| Author | Minnen, David Singh, Saurabh |
| Author_xml | – sequence: 1 givenname: David surname: Minnen fullname: Minnen, David organization: Google Research,Mountain View,CA,USA,94043 – sequence: 2 givenname: Saurabh surname: Singh fullname: Singh, Saurabh organization: Google Research,Mountain View,CA,USA,94043 |
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| Snippet | In learning-based approaches to image compression, codecs are developed by optimizing a computational model to minimize a rate-distortion objective. Currently,... |
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| StartPage | 3339 |
| SubjectTerms | Adaptation models Adaptive Entropy Modeling Bit rate Codecs Entropy Image coding Image Compression Neural Networks Predictive models Training |
| Title | Channel-Wise Autoregressive Entropy Models for Learned Image Compression |
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