Adaptive self-quantization in wavelet-based fractal image compression

Abstract

Finding a model to quantize the scale factors in wavelet-based fractal image compression is a complicated issue. To avoid error, it is helpful to model the distribution of the scale factors and quantize them before the computation process by iterated function systems. Traditionally, a fixed model with uniform distribution was frequently adopted. This is not sophisticated enough, however, to quantize these scale factors from errors since, in general, the factors are not uniformly distributed. We propose an adaptive algorithm with self-quantization to overcome this drawback. Except for the functions of adaptation and self-quantification, the approach has the optimal property that the fundamental objective is to reduce the quantization errors.