Share Email Print

Proceedings Paper

Compressed domain image retrieval by comparing vector quantization codebooks
Author(s): Gerald Schaefer
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Image retrieval and image compression are both very active fields of research. Unfortunately, in the past they were pursued independently leading to image indexing methods being both efficient and effective but restricted to uncompressed images. In this paper we introduce an image retrieval technique that operates in the compressed domain of vector quantize images. Vector quantization (VQ) achieves compression by representing image blocks as indices into a codebook of prototype blocks. By realizing that, if images are coded with their own VQ codebook then much of the image information is contained in the codebook itself, we propose the comparison of the codebooks, based on a Modified Hausdorff distance, as a novel method for compressed domain image retrieval. Experiments, based on an image database comprising many colorful pictures show this technique to give excellent results, outperforming classical color indexing techniques.

Paper Details

Date Published: 4 January 2002
PDF: 8 pages
Proc. SPIE 4671, Visual Communications and Image Processing 2002, (4 January 2002); doi: 10.1117/12.453018
Show Author Affiliations
Gerald Schaefer, The Nottingham Trent Univ. (United Kingdom)

Published in SPIE Proceedings Vol. 4671:
Visual Communications and Image Processing 2002
C.-C. Jay Kuo, Editor(s)

© SPIE. Terms of Use
Back to Top