Low-complexity fractal-based image compression using two-stage search strategy

Abstract

A fractal image compression scheme is presented which incorporates the vector quantizer (VQ) to reduce the computational complexity in the encoding phase. The high-detail blocks of an input image are approximated by a fast fractal coding, and the low-detail blocks are encoded by the VQ technique. In fractal image coding, most of the time is spent on finding a close match between a range block and a large pool of domain blocks. We propose a two-stage search algorithm to reduce the search space of the domain pool. First, a fast and simple binary pattern matching algorithm is proposed to extract a small pool of candidate domains, and then the gray-level-matching algorithm is used to find the best match from the candidates. In addition, a variable block-size segmentation method is used in our coding system to further improve the image quality. It is shown that the average encoding time is short, and the average speedup factor of 45 is achieved, as compared to the full search fractal coding. Simulation results also show that the proposed scheme can achieve better image quality and higher compression ratio than other fast fractal-based coding schemes.