Image Segmentation Using Human Visual System Properties With Applications In Image Compression

Abstract

Many image compression techniques involve segmentation of a gray level image. With such techniques, information is extracted that describes the regions in the segmented image, and this information is then used to form a coded version of the image. In this paper we present a region-growing-based segmentation technique that incorporates human visual system properties, and describe the use of this technique in image compression. We also discuss the effect of requantizing a segmented image. Requantization of a segmented image is useful because it can lead to a reduction in the number of bits required to code the description of the regions in the segmented image. This results in a lower data rate. We show that the number of gray levels in a segmented image can be reduced by a factor of at least twelve, without noticeable degradation in the quality of the segmented image. This result is attributable to human visual system properties having to do with contrast sensitivity, and to the fact that requantization of a segmented image does not usually reduce significantly the number of distinct segments in the image. In addition, in this paper we explore the relationship between the number of segments in an image, and the extent of requantization possible before noticeable degradation occurs in the image. Finally, we discuss the impact of the above results on image compression algorithms, and present some experimental results.