Mahmoud R. El-Sakka and Mohamed S. Kamel, "Adaptive Image
Compression Based on Segmentation and Block Classification",
International Journal of Imaging Systems and Technology,
Special Issue on Image and Video Compression Vol. 10, pp.
33-46, January 1999.
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 it 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. These classes include, a smooth class, a
textural class, and an edge class. Blocks in each class are
separately encoded. For smooth blocks, a new adaptive
prediction technique is used to encode block averages.
Meanwhile, an optimized DCT-based technique is used to encode
both edge and textural blocks.
Based on extensive testing and comparisons with other existing
compression techniques, 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.