Deep Image Compression via End-to-End Learning
This work addresses image compression for applications requiring high visual quality, though it is incremental as it builds on existing CNN-based approaches with specific improvements.
The paper tackles the problem of lossy image compression by developing a deep convolutional neural network method that outperforms existing standards like BPG, WebP, JPEG2000, and JPEG in terms of MS-SSIM at the same bit rate, achieving averaged BD-rate reductions of 7.81% and 19.1% over BPG on two datasets.
We present a lossy image compression method based on deep convolutional neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and JPEG as measured via multi-scale structural similarity (MS-SSIM), at the same bit rate. Currently, most of the CNNs based approaches train the network using a L2 loss between the reconstructions and the ground-truths in the pixel domain, which leads to over-smoothing results and visual quality degradation especially at a very low bit rate. Therefore, we improve the subjective quality with the combination of a perception loss and an adversarial loss additionally. To achieve better rate-distortion optimization (RDO), we also introduce an easy-to-hard transfer learning when adding quantization error and rate constraint. Finally, we evaluate our method on public Kodak and the Test Dataset P/M released by the Computer Vision Lab of ETH Zurich, resulting in averaged 7.81% and 19.1% BD-rate reduction over BPG, respectively.