中国物理B ›› 2011, Vol. 20 ›› Issue (10): 104202-104202.doi: 10.1088/1674-1056/20/10/104202

• CLASSICAL AREAS OF PHENOMENOLOGY • 上一篇    下一篇

An improved fast fractal image compression using spatial texture correlation

王兴元, 王远星, 云娇娇   

  1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
  • 收稿日期:2011-03-02 修回日期:2011-05-06 出版日期:2011-10-15 发布日期:2011-10-15
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant Nos. 60573172 and 60973152), the Superior University Doctor Subject Special Scientific Research Foundation of China (Grant No. 20070141014), and the Natural Science Foundation of Liaoning Province of China (Grant No. 20082165).

An improved fast fractal image compression using spatial texture correlation

Wang Xing-Yuan(王兴元)†, Wang Yuan-Xing(王远星), and Yun Jiao-Jiao(云娇娇)   

  1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
  • Received:2011-03-02 Revised:2011-05-06 Online:2011-10-15 Published:2011-10-15
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant Nos. 60573172 and 60973152), the Superior University Doctor Subject Special Scientific Research Foundation of China (Grant No. 20070141014), and the Natural Science Foundation of Liaoning Province of China (Grant No. 20082165).

PDF (PC)

1292

赞

摘要： This paper utilizes a spatial texture correlation and the intelligent classification algorithm (ICA) search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image; in comparison with a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same.

Abstract: This paper utilizes a spatial texture correlation and the intelligent classification algorithm (ICA) search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image; in comparison with a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same.

Key words: fractal image compression, texture features, intelligent classification algorithm, spatial correlation

中图分类号:  (Image forming and processing)

  • 42.30.Va
42.30.Wb (Image reconstruction; tomography) 87.57.C- (Image quality)