Paper:
Language: English:

Effective Multiple Vector Quantization for Image Compression

Noritaka Shigei^, Hiromi Miyajima^, Michiharu Maeda^,
and Lixin Ma^*

^Dept. of Electrical and Electronics Engineering, Kagoshima University, 1-21-40 Korimoto, Kagoshima-shi 890-0065, Japan
^Fukuoka Institute of Technology, 3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka 811-0295, Japan
^University of Shanghai for Science and Technology, 516 Jungong Rd, Shanghai 200093, China

Received: October 29, 2006

Accepted: June 30, 2007

Keywords: vector quantization, image compression, competitive learning, multiple codebooks, compression rate

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.11, No.10 pp. 1189-1196, 2007

Abstract

Multiple-VQ methods generate multiple independent codebooks to compress an image by using a neural network algorithm. In the image restoration, the methods restore low quality images from the multiple codebooks, and then combine the low quality ones into a high quality one. However, the naive implementation of these methods increases the compressed data size too much. This paper proposes two improving techniques to this problem: “index inference” and “ranking based index coding.” It is shown that index inference and ranking based index coding are effective for smaller and larger codebook sizes, respectively.

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