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Proceedings Paper

Simple look-up-table algorithms to lower the bit rate of AMBTC for image coding
Author(s): Chun-He Liu; Zhe-Ming Lu; Sheng-He Sun
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Paper Abstract

Block truncation coding (BTC) is an efficient lossy image compression technique, which has the advantage of being easy to implement compared to other block based compression techniques such as transform coding and vector quantization. The principle of the original BTC method is to preserve the block mean and the block standard deviation. Lema and Mitchell present absolute moment BTC (AMBTC) that preserves the higher mean and the lower mean and minimizes the MSE value among the BTC variants that use the mean value as the quantization threshold. However, the bit rate achieved with the original BTC algorithm or the AMBTC algorithm is 2bits/pixel. In this paper, we introduce two simple look- up-table algorithms to code the higher mean and the lower mean of AMBTC, one can reduce the bit rate without any extra distortion, and the other can reduce more bit rate with a little extra distortion. The main idea of the two proposed algorithms is to encode the higher mean and the lower mean together as a mean pair. These two algorithms can be combined with the prediction and interpolation techniques that are used to code the bit plane of BTC to further reduce the total bit rate of AMBTC. We denote the two algorithms as LUTBTC-1 and LUTBTC-2. These two algorithms are used to encode 256-gray images including remote-sensed images. Test results show that the LUTBTC-1 algorithm has the same PSNR as the AMBTC algorithm but has lower bit rate compared to the AMBTC algorithm. The LUTBTC-2 algorithm has a little extra degradation in image quality but has lower bit rate than the AMBTC algorithm and the LUTBTC-1 algorithm. Both LUTBTC-1 and LUTBTC-2 have higher encoding quality than BTC- VQ (the algorithm that uses vector quantization (VQ) to code the mean pairs without using VQ to code the bit plane), and LUTBTC-2 also has lower bit rate than BTC-VQ for ordinary images (not for remote sensed images).

Paper Details

Date Published: 26 September 2001
PDF: 6 pages
Proc. SPIE 4551, Image Compression and Encryption Technologies, (26 September 2001); doi: 10.1117/12.442886
Show Author Affiliations
Chun-He Liu, Harbin Institute of Technology (China)
Zhe-Ming Lu, Harbin Institute of Technology (China)
Sheng-He Sun, Harbin Institute of Technology (China)

Published in SPIE Proceedings Vol. 4551:
Image Compression and Encryption Technologies
Jun Tian; Tieniu Tan; Liangpei Zhang, Editor(s)

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