Neural networks for classified vector quantization of images

Yong Ho Shin; Cheng-Chang Lu

doi:10.1117/12.58319

19 May 1992 Neural networks for classified vector quantization of images

Yong Ho Shin, Cheng-Chang Lu

Proceedings Volume 1657, Image Processing Algorithms and Techniques III; (1992) https://doi.org/10.1117/12.58319
Event: SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, 1992, San Jose, CA, United States

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.

Citation Download Citation

Yong Ho Shin and Cheng-Chang Lu "Neural networks for classified vector quantization of images", Proc. SPIE 1657, Image Processing Algorithms and Techniques III, (19 May 1992); https://doi.org/10.1117/12.58319

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