Improved Variance-Based Fractal Image Compression Using Neural Networks

Although the baseline fractal image encoding algorithm could obtain very high compression ratio in contrast with other compression methods, it needs a great deal of encoding time, which limits it to widely practical applications. In recent years, an accelerating algorithm based on variance is addressed and has shortened the encoding time greatly; however, in the meantime, the image fidelity is obviously diminished. In this paper, a neural network is utilized to modify the variance-based encoding algorithm, which makes the quality of reconstructed images improved remarkably as the encoding time is significantly reduced. Experimental results show that the reconstructed images quality measured by peak-signal-to-noise-ratio is better than conventional variance-based algorithm, while the time consumption for encoding and the compression ratio are almost the same as the conventional variance-based algorithm.

Authors and Affiliations

1. Department of Automation, Tsinghua University, Beijing, 100084, China

   Yiming Zhou, Chao Zhang & Zengke Zhang

Editors and Affiliations

1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

   Jun Wang

2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

   Zhang Yi

3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

   Jacek M. Zurada

4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

   Bao-Liang Lu

5. School of Electrical and Electronic Engineering, University of Manchester, UK

   Hujun Yin

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