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High-performance compression of visual information-a tutorial review. I. Still pictures
Egger, O.; Fleury, P.; Ebrahimi, T.; Kunt, M.;
Proceedings of the IEEE
Volume 87,
Issue 6,
June 1999
Page(s):976
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1013
Abstract:
Digital images have become an important source of information in the modern world of communication systems. In their raw form, digital images require a tremendous amount of memory. Many research efforts have been devoted to the problem of image compression in the last two decades. Two different compression categories must be distinguished: lossless and lossy. Lossless compression is achieved if no distortion is introduced in the coded image. Applications requiring this type of compression include medical imaging and satellite photography. For applications such as video telephony or multimedia applications, some loss of information is usually tolerated in exchange for a high compression ratio. In this two-part paper, the major building blocks of image coding schemes are overviewed. Part I covers still image coding, and Part II covers motion picture sequences. In this first part, still image coding schemes have been classified into predictive, block transform, and multiresolution approaches. Predictive methods are suited to lossless and low-compression applications. Transform-based coding schemes achieve higher compression ratios for lossy compression but suffer from blocking artifacts at high-compression ratios. Multiresolution approaches are suited for lossy as well for lossless compression. At lossy high-compression ratios, the typical artifact visible in the reconstructed images is the ringing effect. New applications in a multimedia environment drove the need for new functionalities of the image coding schemes. For that purpose, second-generation coding techniques segment the image into semantically meaningful pairs. Therefore, parts of these methods have been adapted to work for arbitrarily shaped regions. In order to add another functionality, such as progressive transmission of the information, specific quantization algorithms must he defined. A final step in the compression scheme is achieved by the codeword assignment. Finally, coding results are presented which compare state-of-the-art techniques for lo
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