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.
Highlights
With the rapid development of the Internet and intellectual mobile terminals, multimedia video and image applications are becoming more and more widespread
The traditional fractal video compression method performs frame-by-frame compression on the entire video without considering spatial-temporal similarity, which causes a lot of computational redundancy [7]
We propose a novel fractal video compression method
Summary
With the rapid development of the Internet and intellectual mobile terminals, multimedia video and image applications are becoming more and more widespread. In order to effectively store and manage big video data, efficient video compression technology has become crucial. The traditional fractal video compression method performs frame-by-frame compression on the entire video without considering spatial-temporal similarity, which causes a lot of computational redundancy [7]. We propose a novel fractal video compression method. In order to compress real-time big video data, we propose a video compression framework with a dual-layer parallel structure.
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