Weighted centroid neural network for edge preserving image compression

An edge preserving image compression algorithm based on an unsupervised competitive neural network is proposed. The proposed neural network, the called weighted centroid neural network (WCNN), utilizes the characteristics of image blocks from edge areas. The mean/residual vector quantization (M/RVQ)...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2001-09, Vol.12 (5), p.1134-1146
Main Authors: Park, D C, Woo, Y J
Format: Article
Language:English
Subjects:
Algorithms
Bit rate
Centroids
Decoding
Degradation
Distortion measurement
Image coding
Image compression
Image reconstruction
Mathematical analysis
Neural networks
Preserving
Studies
Transform coding
Unsupervised learning
Vector quantization
Vectors (mathematics)
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Summary:An edge preserving image compression algorithm based on an unsupervised competitive neural network is proposed. The proposed neural network, the called weighted centroid neural network (WCNN), utilizes the characteristics of image blocks from edge areas. The mean/residual vector quantization (M/RVQ) scheme is utilized in this proposed approach as the framework of the proposed algorithm. The edge strength of image block data is utilized as a tool to allocate the proper code vectors in the proposed WCNN. The WCNN successfully allocates more code vectors to the image block data from edge area while it allocates less code vectors to the image black data from shade or non-edge area when compared to conventional neural networks based on VQ algorithm. As a result, a simple application of WCNN to an image compression problem gives improved edge characteristics in reconstructed images over conventional neural network based on VQ algorithms such as self-organizing map (SOM) and adaptive SOM.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/72.950142