Fractal image compression using pyramids

grantor: University of TorontoFractal image compression is an attractive technique for image coding because of its distinct features and the low bit-rate requirement. In this research, several techniques are introduced to improve the compression performance. Fractal image compression is based on the self-similarity search of the image. The encoding process is computationally intensive. In this thesis, a pyramidal framework is proposed to reduce the encoding complexity. The encoding complexity is reduced by as much as two orders of magnitude. Because the domain-range matching is independent, parallel methods are proposed to further speed up the encoding process. As with any lossy compression schemes, one of the challenges is either to maximize the image quality at a fixed bit rate, or to minimize the rate required for a given quality. To fulfil this purpose, the constant contractive factor in conventional fractal image compression is extended to the case of nonlinear contractive functions, which lead to significantly better reconstructed images and faster decoding than the conventional fractal method. Furthermore, as digital images and video products are designed for human viewing, human visual system (HVS) models are exploited to faithfully reproduce perceptually important information and eliminate the information that the visual system cannot perceive. Based on the human visual system's nonlinear response to luminance and the visual masking effects, a perceptually appropriate metric is defined. The psychophysical raw data on the visual contrast threshold is first interpolated as a function of background luminance and visual angle, and is then used as an error upper bound for perceptually based fractal image compression. The perceptually based method produces visually better reconstructed images than the conventional method and the JPEG standard. To reduce the domain search complexity, the pyramidal search method is also extended for perceptually based fractal image compression. Practical fractal image compression is a block coding. At very low bit rates, fractal method encoded images may exhibit blocking artifacts. Based on the Laplacian pyramidal representation of the image, the thesis presents a general post-processing method to remove the blocking effects.Ph.D