Regular Articles

Adaptive technique for image compression based on vector quantization using a self-organizing neural network

[+] Author Affiliations
Vijayan K. Asari

Computational Intelligence and Machine Vision Laboratory, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, Virginia 23529

J. Electron. Imaging. 14(2), 023009 (May 12, 2005). doi:10.1117/1.1901678
History: Received Jul. 11, 2003; Revised Feb. 27, 2004; Accepted Nov. 11, 2004; May 12, 2005; Online May 12, 2005
Text Size: A A A

A novel image compression technique employing the self-organized clustering capability of Fuzzy-ART neural network and 2D runlength encoding is presented. Initially the image is divided into 4×4 blocks and the 16 element vectors representing the pixels in the blocks are applied to the Fuzzy-ART network for classification. The image is then represented by the block codes consisting of the sequence of class indices, and the codebook consisting of the class index and their respective gray levels. Further compression is achieved by 2D runlength encoding, making use of the repetitions of the class index in the block codes in x and y directions. By controlling the vigilance parameter of Fuzzy-ART, a reasonable compression of the image without sacrificing the image quality can be obtained. From the experimental results, it can be seen that the proposed method of image compression can be used for image communication systems where large compression ratio is required. An efficient technique for automatic computation of the value of vigilance parameter based on the image characteristics for optimum compression of the image is also presented in this paper. With the introduction of a new class of Fuzzy-ART network, namely Force Class Fuzzy-ART, hardware implementation of the image compression module is made feasible. This architecture constrains the maximum number of classes in the output of the network by forcing the new vectors into one of the closest categories. © 2005 SPIE and IS&T.

© 2005 SPIE and IS&T

Citation

Vijayan K. Asari
"Adaptive technique for image compression based on vector quantization using a self-organizing neural network", J. Electron. Imaging. 14(2), 023009 (May 12, 2005). ; http://dx.doi.org/10.1117/1.1901678


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.