Parallel Fractal Compression Method for Big Video Data

Author:

Liu Shuai123ORCID,Bai Weiling12ORCID,Liu Gaocheng12,Li Wenhui3ORCID,Srivastava Hari M.45ORCID

Affiliation:

1. College of Computer Science, Inner Mongolia University, Hohhot, 010012, China

2. Inner Mongolia Key Laboratory of Social Computing and Data Processing, Inner Mongolia University, Hohhot, 010012, China

3. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China

4. Department of Mathematics and Statistics, University of Victoria, Victoria, British Columbia, Canada V8W 3R4

5. Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan

Abstract

With the development of technologies such as multimedia technology and information technology, a great deal of video data is generated every day. However, storing and transmitting big video data requires a large quantity of storage space and network bandwidth because of its large scale. Therefore, the compression method of big video data has become a challenging research topic at present. Performance of existing content-based video sequence compression method is difficult to be effectively improved. Therefore, in this paper, we present a fractal-based parallel compression method without content for big video data. First of all, in order to reduce computational complexity, a video sequence is divided into several fragments according to the spatial and temporal similarity. Secondly, domain and range blocks are classified based on the color similarity feature to reduce computational complexity in each video fragment. Meanwhile, a fractal compression method is deployed in a SIMD parallel environment to reduce compression time and improve the compression ratio. Finally, experimental results show that the proposed method not only improves the quality of the recovered image but also improves the compression speed by compared with existing compression algorithms.

Funder

Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

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