Image Compression Based on Fractal with Classification by Vector Quantization

Abstract

The conventional fractal encoding algorithm performs an exhaustive search to find a close match between a range block and a large pool of domain blocks. For a large image, the domain pool increases obviously so the encoding time will also increase. In this paper, we propose a hybrid scheme by combining the fractal image compression with the vector quantization. We use the longest distance first algorithm to classify the domain blocks. In this way, we can reduce the range in searching the domain pool Experiment results show that our method can effectively speed up the encoding time about ten times. In addition, the quality of our reconstructed images is still as good as the conventional fractal algorithm.