Abstract
Vector quantization is one approach to image compression, the coding of an image so as to preserve the maximum possible quality subject to the available storage or communication capacity. In its most general form, vector quantization includes most algorithms for data compression as structured special cases. This paper is intended as a survey of image compression techniques from the viewpoint of vector quantization. A variety of approaches are described and their relative advantages and disadvantages considered. Our primary focus is on the family of tree-structured vector quantizers, which we believe provide a good balance of performance and simplicity. In addition, we show that with simple modifications of the design technique, this form of compression can incorporate image enhancement and local classification in a natural manner. This can simplify subsequent digital signal processing and can, at sufficient bit rates, result in images that are actually preferred to the originals.
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Gray, R.M., Cosman, P.C., Riskin, E.A. (1992). Image Compression and Tree-Structured Vector Quantization. In: Storer, J.A. (eds) Image and Text Compression. The Kluwer International Series in Engineering and Computer Science, vol 176. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-3596-6_1
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DOI: https://doi.org/10.1007/978-1-4615-3596-6_1
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