Vector quantization with edge reconstruction

This paper proposes a vector quantization (VQ) scheme that improves the quality of the reconstructed image by correcting quantization artifacts. The basic idea of the coding scheme is to treat separately the blocks containing edges (edge blocks), as they contain important perceptual information. The edge blocks are identified using an edge detection algorithm and are labeled by an escape index, and their reconstruction is done via an interpolation procedure that exploits the spatial correlation in the image. The remaining blocks (smooth blocks) are coded using standard VQ.

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Nasser M. Nasrabadi,et al.  Edge-based subband VQ techniques for images and video , 1994, IEEE Trans. Circuits Syst. Video Technol..

[3]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[4]  Ruey-Feng Chang,et al.  Adaptive edge-based side-match finite-state classified vector quantization with quadtree map , 1996, IEEE Trans. Image Process..

[5]  R. Gray,et al.  Using vector quantization for image processing , 1993, Proc. IEEE.

[6]  H. W. Park,et al.  Blocking effect reduction of JPEG images by signal adaptive filtering , 1998, IEEE Trans. Image Process..

[7]  Martin D. Levine,et al.  Vision in Man and Machine , 1985 .