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arXiv · 1705.06687

Target-Quality Image Compression with Recurrent, Convolutional Neural Networks

Michele Covell·Nick Johnston·David Minnen·Sung Jin Hwang·Joel Shor·Saurabh Singh·Damien Vincent·George Toderici
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Abstract

We introduce a stop-code tolerant (SCT) approach to training recurrent convolutional neural networks for lossy image compression. Our methods introduce a multi-pass training method to combine the training goals of high-quality reconstructions in areas around stop-code masking as well as in highly-detailed areas. These methods lead to lower true bitrates for a given recursion count, both pre- and post-entropy coding, even using unstructured LZ77 code compression. The pre-LZ77 gains are achieved by trimming stop codes. The post-LZ77 gains are due to the highly unequal distributions of 0/1 codes from the SCT architectures. With these code compressions, the SCT architecture maintains or exceeds the image quality at all compression rates compared to JPEG and to RNN auto-encoders across the Kodak dataset. In addition, the SCT coding results in lower variance in image quality across the extent of the image, a characteristic that has been shown to be important in human ratings of image quality

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Michele Covell, Nick Johnston, David Minnen, Sung Jin Hwang, Joel Shor, Saurabh Singh, Damien Vincent, George Toderici. 2017-05-18. Target-Quality Image Compression with Recurrent, Convolutional Neural Networks. https://arxiv.org/abs/1705.06687

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