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Proceedings Paper

Variable block-size interpolative vector quantization
Author(s): Krit Panusopone; K. R. Rao
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Paper Abstract

The conventional fixed block-size vector quantization (VQ) usually copes with a small dimension data to alleviate computation load. This technique suffers from the blocking effect at low bit rates. To handle this problem, input data is arranged to form a variable dimension vector so that correlation between two vectors is weak. This paper uses quadtree partitioning to form variable block-size regions. Instead of taking all data of segmented area directly, only constant amount of pixels are selectively subsampled from each terminal node to form an input vector of VQ. With this improvement, only single universal codebook can take care of all kinds of image data. At the decoder, reduced dimension vector will be interpolated back to its full resolution information. Simulation results show that the reconstructed images preserve fine and pleasant qualities in both edge and background regions. The search time for VQ coder also reduces significantly. Furthermore, the comparison of the PSNR of the reconstructed images also reveals better performance of the proposed method than the traditional fixed block-size scheme at low bit rates.

Paper Details

Date Published: 27 February 1996
PDF: 9 pages
Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); doi: 10.1117/12.233246
Show Author Affiliations
Krit Panusopone, Univ. of Texas/Arlington (United States)
K. R. Rao, Univ. of Texas/Arlington (United States)


Published in SPIE Proceedings Vol. 2727:
Visual Communications and Image Processing '96
Rashid Ansari; Mark J. T. Smith, Editor(s)

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