Learning Better Lossless Compression Using Lossy Compression
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Date
2020
Publication Type
Conference Paper
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yes
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Abstract
We leverage the powerful lossy image compression algorithm BPG to build a lossless image compression system. Specifically, the original image is first decomposed into the lossy reconstruction obtained after compressing it with BPG and the corresponding residual. We then model the distribution of the residual with a convolutional neural network-based probabilistic model that is conditioned on the BPG reconstruction, and combine it with entropy coding to losslessly encode the residual. Finally, the image is stored using the concatenation of the bitstreams produced by BPG and the learned residual coder. The resulting compression system achieves state-of-the-art performance in learned lossless full-resolution image compression, outperforming previous learned approaches as well as PNG, WebP, and JPEG2000.
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published
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2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Pages / Article No.
6637 - 6646
Publisher
IEEE
Event
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020) (virtual)
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Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.