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. Once the image is segmented, information is extracted describing the shapes and interiors of the image segments. Compression is achieved by efficiently representing the image segments. An image segmentation technique is proposed which is based on centroid-linkage region growing, and takes advantage of human visual systems (HVS) properties. The parameters for the segmentation algorithm which produce the most visually pleasing segmented images, and demonstrate the effectiveness of the method are systematically determined through subjective experiments. A method is also proposed for the quantization of segmented images based on HVS contrast sensitivity, and investigate the effect of quantization on segmented images. These segmentation and quantization methods are applied in a new compression technique which fits into the category commonly known as second generation image compression methods. The compression method is designed for application single-frame images. Other segmentation-based image compression techniques have typically represented the image segments by encoding the boundaries of the segments. The use of morphological skeletons is proposed to represent the segments. The morphological skeleton of an image is similar to the medial axis. The application of mathematical morphology is described to generate skeletons for the image segments, and the advantages and disadvantages of using morphological skeletons in segmentation-based image compression.
- Pub Date:
- Data Compression;
- Imaging Techniques;
- Electronics and Electrical Engineering