Previous [ 1] [ 2] [ 3] [ 4] [ 5] [ 6] [ 7] [ 8] [ 9] [ 10] [ 11] [ 12] [ 13] [ 14] [ 15] [ 16] [ 17] [ 18] [ 19]

@

Journal of Information Science and Engineering, Vol. 24 No. 5, pp. 1579-1591 (September 2008)

A Lossless Image Coder Integrating Predictors and Block-Adaptive Prediction

Feng-Yang Hsieh1, Chia-Ming Wang2, Chun-Chieh Lee2 and Kuo-Chin Fan2,3
1Department of Computer Science and Information Engineering
Ta Hwa Institute of Technology
Hsinchu, 307 Taiwan
2Institute of Computer Science and Information Engineering
National Central University
Chungli, 320 Taiwan
3Department of Informatics
Fo Guang University
Yilan, 262 Taiwan

This paper proposes a lossless image compression scheme integrating well-known predictors and Minimum Rate Predictor (MRP). Minimum Rate Predictor is considered as one of the most successful method in coding rates for lossless grayscale image compression so far. In the proposed method, the linear predictor is designed as the combination of causal neighbors together with well-known predictors (GAP, MED, and MMSE) to improve coding rates. To further reduce the residual entropy, we also redesign the calculation of context quantization and the disposition of neighboring pixels. The modifications made in our proposed method are crucial in enhancing the compression ratios. Experimental results demonstrate that the coding rates of the proposed method are lower than those of MRP and other state-of-the-art lossless coders among most of the test images. In addition, the residual entropy of the proposed scheme in the first iteration is lower than that of MRP and is relatively closer to the final residual entropy than that in MRP. This phenomenon will allow our proposed scheme to be terminated in less iterations while maintaining a relatively good compression performance.

Keywords: lossless image compression, minimum rate predictor, adaptive predictor, residual entropy, image coder

Full Text () Retrieve PDF document (200809_19.pdf)

Received November 28, 2006; revised June 20 & October 29, 2007; accepted January 10, 2008.
Communicated by Liang-Gee Chen.