Mahmoud R. El-Sakka and Mohamed S. Kamel, "Adaptive Image Compression Based on Segmentation and Block Classification", IEEE International Conference on Image Processing, ICIP'1998, Vol. 2, pp. 555-559, October 1998, Chicago, Illinois, USA.

Abstract

This paper presents a new digital image compression scheme which exploits one of the human visual system properties---namely that of, recognizing images by their regions---to achieve high compression ratios. It also assigns a variable bit-count to each image region that is proportional to the amount of information the region conveys to the viewer. The new scheme copes with image non-stationarity by adaptively segmenting the image into variable-block sized regions, and classifying them into statistically and perceptually different classes. Then, blocks in each class are separately encoded.

Based on extensive testing, the performance of the new scheme surpasses the performance of the JPEG standard and goes beyond its compression limits. In most test cases, the new compression scheme results in a maximum compression ratio that is at least twice of JPEG, while exhibiting lower objective and subjective image degradations. Moreover, the performance of the new block-based compression is comparable to the performance of the state-of-the-art wavelet-based compression technique and provides a good alternative when adaptability to image content is of interest.