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Proceedings Paper

Neural networks for classified vector quantization of images
Author(s): Yong Ho Shin; Cheng-Chang Lu
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Paper Abstract

Recently, vector quantization (VQ) has received considerable attention and become an effective tool for image compression. It provides high compression ratios and simple decoding processes. However, studies on practical implementation of VQ have revealed some major difficulties such as edge integrity and codebook design efficiency. Over the past few years, a new wave of research in neural networks has emerged. Neural networks models have provided an effective alternative to solving computationally intensive problems. In this paper, we propose to implement VQ for image compression based on neural networks. Separate codebooks for edge and background blocks are designed using Kohonen self-organizing feature maps to preserve edge integrity and improve the efficiency of codebook design. Improved image quality has bee achieved and the comparability of new attempts with existing VQ approaches has been demonstrated with experimental results.

Paper Details

Date Published: 19 May 1992
PDF: 6 pages
Proc. SPIE 1657, Image Processing Algorithms and Techniques III, (19 May 1992); doi: 10.1117/12.58319
Show Author Affiliations
Yong Ho Shin, Kent State Univ. (United States)
Cheng-Chang Lu, Kent State Univ. (United States)


Published in SPIE Proceedings Vol. 1657:
Image Processing Algorithms and Techniques III
James R. Sullivan; Benjamin M. Dawson; Majid Rabbani, Editor(s)

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