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Proceedings Paper

Joint image compression and indexing technique using wavelet transform
Author(s): Hai Wei; David Y. Y. Yun
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Paper Abstract

In this paper, a joint image compression and indexing technique using wavelet transform is presented. For compression, scalar quantization and classified vector quantization are applied in the wavelet domain to remove redundancies from different sub-bands according to their distinct characteristics. For indexing, two statistical feature vectors are constructed directly from compression outputs (quantized sub-band data before entropy coding), which facilitate a hierarchical (coarser to finer) indexing procedure and achieve image indexing in the compressed domain. Experimental results show that the joint technique performs with equal effectiveness as either compression or indexing standing alone, while the computational cost for decompression is greatly reduced (only entropy decoding is needed). Thus, the advantages of this joint (dual) image compression-indexing technique and its feasibility for online distributed image retrieval in the arena of exploding networked image applications are demonstrated.

Paper Details

Date Published: 30 January 2003
PDF: 8 pages
Proc. SPIE 4793, Mathematics of Data/Image Coding, Compression, and Encryption V, with Applications, (30 January 2003); doi: 10.1117/12.453522
Show Author Affiliations
Hai Wei, Univ. of Hawaii at Manoa (United States)
David Y. Y. Yun, Univ. of Hawaii at Manoa (United States)


Published in SPIE Proceedings Vol. 4793:
Mathematics of Data/Image Coding, Compression, and Encryption V, with Applications
Mark S. Schmalz, Editor(s)

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