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Texture- and multiple-template-based algorithm for lossless compression of error-diffused images.

Yong-Huai Huang1, Kuo-Liang Chung

  • 1Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan 10672, ROC.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|May 12, 2007
PubMed
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A new texture- and multiple-template-based (TM-based) algorithm significantly improves lossless compression for error-diffused images. This method achieves higher compression ratios with minimal encoding time increases compared to existing standards and algorithms.

Area of Science:

  • Computer Science
  • Image Processing
  • Data Compression

Background:

  • Context-based arithmetic coding is effective for lossless compression of error-diffused images.
  • Existing methods show varying degrees of success in compression ratio and encoding time.

Purpose of the Study:

  • To develop a novel and efficient algorithm for lossless compression of error-diffused images.
  • To improve compression ratios while maintaining acceptable encoding times.

Main Methods:

  • A block- and texture-based approach trains multiple templates using representative texture features.
  • An adaptive TM-based algorithm selects the best template for each image block based on its texture.

Main Results:

  • The TM-based algorithm shows a 24% compression improvement over JBIG and 19.4% over BACIC.

Related Experiment Videos

  • It achieves 17.6% improvement over Lee and Park's algorithm with minimal encoding time increase.
  • Compression ratio (1.60) is competitive with the free tree-based algorithm (1.62), with significantly reduced encoding time (0.995 s vs. 109.131 s).

    • Conclusions:

    • The proposed TM-based algorithm offers a superior balance of compression ratio and encoding efficiency for error-diffused images.
    • This method provides a competitive and efficient solution for lossless image compression challenges.