A NEW IMAGE COMPRESSION METHOD USING DYNAMIC PREDICTOR BASED ON CURRENT CONTEXT

Abstract

In this paper, a new image compression method is proposed. The approach first provides a decomposition method to segment the gray values of an image into several parts called buckets. The decomposition rule is to make, as much as possible, each bucket contain only one nearly-homogeneous region or edge area. For each pixel P, if the gray values of its three neighbors (top-left, top, left) belong to buckets B1, B2, and B3, respectively, then (B1, B2, B3) is referred to as P's context. An individual predictor is then derived for each set of pixels with the same context. The gray value of each pixel is then estimated by the predictor corresponding to its context. Some degree of distortion is allowed. A pixel with prediction error less than a preset value is considered as a redundant point. An image is finally coded by the prediction errors of nonredundant pixels and by the number of consecutive redundant pixels. Both of these two kinds of values are quantized and coded by their own adaptive Huffman codes. Experimental results show that the proposed method not only possesses high-speed computing property but also provides high compression ratio and good quality.