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Proceedings Paper

Low bit-rate image compression via discrete wavelet transform and classified vector quantization
Author(s): Huijie Guo; Baojun Zhao
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Paper Abstract

This paper proposes a high-efficiency low bit-rate image compression algorithm that is based on weighted classified vector quantization (WCVQ) of discrete wavelet transform (DWT) coefficients. The algorithm first does multi-level wavelet decomposition to the original image, and then constructs band-cross vectors along resolution-level-cross high frequency sub-bands respectively in the horizontal, vertical and diagonal three directions that makes the most of the correlation of DWT coefficients among those sub-bands, and then classifies these band-cross vectors into important clustering and unimportant clustering by zero-tree vector and vector energy, and last merges several adjacent unimportant vectors into an unimportant vector block and codes them uniformly, while, applies weighted vector quantization consistent with HVS to the important vectors by progressive constructive clustering (PCC) algorithm that improves the coding efficiency and the reconstructed image quality.

Paper Details

Date Published: 1 October 2011
PDF: 6 pages
Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 828543 (1 October 2011); doi: 10.1117/12.913244
Show Author Affiliations
Huijie Guo, Beijing Institute of Technology (China)
Baojun Zhao, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 8285:
International Conference on Graphic and Image Processing (ICGIP 2011)
Yi Xie; Yanjun Zheng, Editor(s)

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