In order to represent a digital image, a very large number of bits is required. For example, a 512 X 512 pixel, 256 gray level image requires over two million bits. This large number of bits is a substantial drawback when it is necessary to store or transmit a digital image. Image compression, often referred to as image coding, attempts to reduce the number of bits used to represent an image, while keeping the degradation in the decoded image to a minimum. One approach to image compression is segmentation-based image compression. The image to be compressed is segmented, i.e. the pixels in the image are divided into mutually exclusive spatial regions based on some criteria. Once the image has been segmented, information is extracted describing the shapes and interiors of the image segments. Compression is achieved by efficiently representing the image segments. In this thesis we propose an image segmentation technique which is based on centroid-linkage region growing, and takes advantage of human visual system (HVS) properties. We systematically determine through subjective experiments the parameters for our segmentation algorithm which produce the most visually pleasing segmented images, and demonstrate the effectiveness of our method. We also propose a method for the quantization of segmented images based on HVS contrast sensitivity, arid investigate the effect of quantization on segmented images.

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