Image coding by using non-linear texture decomposition and image summarization

Abstract

Total variation regularization can be used to decompose any natural image into a structure image (edges and smooth areas) and a texture image (texture only). Although structure images can be highly compressed by conventional JPEG methods, texture images require many bits to represent their feature details. Texture images are composed of several repeated patterns, each of which can be synthesized from a small texture image, and the borders of different texture patterns correspond to the edges in a structure image. We present a novel image compression method for these images based on this observation. First, we apply a context-aware resizing method to the input image to obtain a compaction image that has as many unique texture patterns as possible. Then the compaction image is divided into texture and structure images. Our proposed encoder sends the compressed compaction texture and compaction structure images and the compressed structure image extracted from the input image to the decoder side. At the decoder side, a texture image is synthesized from the compaction texture image through matching between the compaction structure image and the original-size structure image. Experimental results show that the decoded image obtained by our proposed method is subjectively similar to the original one, with higher texture feature accuracy than that obtained by a conventional JPEG method but almost the same data size.