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30 September 1996 Edge-preserving vector quantization using a neural network
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Recently the vector quantization (VQ) has received considerable interests as a powerful image data compression technique. However, studies of image coding with VQ have revealed that VQ for image compression suffers from edge degradation in the reproduced images. In this paper, we describe an adaptive learning method of the edge preserving VQ based on Kohonen's self-organizing feature map neural network. The learning procedure is performed by extracting the edge of the whole image, then adaptively adjusting the learning rate that are determined by the edge information of the image block. Compared with direct image VQ coding, the experiment results show the reproduced images quality are well improved, at the same compression ratio.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xujun Ye and Zhineng Li "Edge-preserving vector quantization using a neural network", Proc. SPIE 2898, Electronic Imaging and Multimedia Systems, (30 September 1996);

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