Multi-layered Image Representation: Application to Image Compression

Abstract The main contribution of this work is a new paradigm for image rep-resentation and image compression. We describe a new multi-layered representation technique for images. An image is parsed into a superpo-sition of coherent layers: smooth-regions layer, textures layer, etc. The multi-layered decomposition algorithm consists in a cascade of compres-sions applied successively to the image itself and to the residuals that resulted from the previous compressions. During each iteration of the algorithm, we code the residual part in a lossy way: we only retain the most significant structures of the residual part, which results in a sparse representation. Each layer is encoded independently with a dif-ferent transform, or basis, at a different bitrate; and the combination of the compressed layers can always be reconstructed in a meaningful way. The strength of the multi-layer approach comes from the fact that different sets of basis functions complement each others: some of the basis functions will give reasonable account of the large trend of the data, while others will catch the local transients, or the oscillatory patterns. This multi-layered representation has a lot of beautiful appli-cations in image understanding, and image and video coding. We have implemented the algorithm and we have studied its capabilities