Accelerating Fractal Compression With a Real-Time Decoder

Image data compression by fractal techniques has been widely investigated. Although its high compression ratio and resolution-independent decoding properties are attractive, the encoding process is computationally demanding in order to achieve an optimal compression. A variety of speed-up algorithms have been proposed since Jacquin published a novel fractal coding algorithm. Unfortunately, the quantization strategy of scaling coefficients and the programming techniques lead to the results reported by different researchers are various even on the same image data which causes the speed-up of compression is incomparable. This paper proposes a real-time fractal decoder as a standard. We report the implementation results of a nearly optimal encoding algorithm OPT on commonly used images: Jet, Lenna, Mandrill, and Peppers of size 512×512. An accelerating compression algorithm using maximum gradient MG is shown to be 1300 times faster than OPT with a slight drop of PSNR value when encoding a 512×512 image.