Classified vector quantisation with variable block-size DCT models
M-H. Lee · IEE Proceedings - Vision Image and Signal Processing · 1994
The paper describes the classified vector quantisation (CVQ) of an image, based on quadtrees and a classification technique in the discrete cosine transform (DCT) domain. In this scheme, a quadtree is used to segment low-detail regions into variable sized blocks and high-detail regions into uniform 4*4 blocks of various edge and mixed classes. High-detail blocks are classified by an edge-oriented classifier which employs a pattern-matching technique with edge models defined in the normalised DCT domain. The proposed classifier is simple to implement, and efficiently classifies edges to good visual accuracy. The low-detail regions are encoded at very low bit rates with little perceptual degradation, while the encoding of the high-detail regions is performed to achieve a good perceptual quality in the decoded image. Decoded images of high visual quality are obtained for encoding rates between 0.3 and 0.7 bpp.