JACIII Vol.11 No.10 pp. 1189-1196
doi: 10.20965/jaciii.2007.p1189


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

October 29, 2006
June 30, 2007
December 20, 2007
vector quantization, image compression, competitive learning, multiple codebooks, compression rate
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.
Cite this article as:
N. Shigei, H. Miyajima, M. Maeda, and L. Ma, “Effective Multiple Vector Quantization for Image Compression,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.10, pp. 1189-1196, 2007.
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