Sparse Multi-layer Image Approximation: Facial Image Compression
This work addresses image compression for facial images, offering incremental improvements over existing methods.
The authors tackled the problem of multi-layer image compression for facial images, achieving a considerable performance boost in PSNR at high compression ratios compared to JPEG2000.
We propose a scheme for multi-layer representation of images. The problem is first treated from an information-theoretic viewpoint where we analyze the behavior of different sources of information under a multi-layer data compression framework and compare it with a single-stage (shallow) structure. We then consider the image data as the source of information and link the proposed representation scheme to the problem of multi-layer dictionary learning for visual data. For the current work we focus on the problem of image compression for a special class of images where we report a considerable performance boost in terms of PSNR at high compression ratios in comparison with the JPEG2000 codec.