Parallel Fractal Compression Method for Big Video Data

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, t...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) Vol. 2018; no. 2018; pp. 1 - 16
Main Authors: Li, Wenhui, Liu, Gaocheng, Bai, Weiling, Liu, Shuai, Srivástava, Hari Mohan
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
John Wiley & Sons, Inc
Wiley
Subjects:
Algorithms
Analysis
Complexity
Compression ratio
Computation
Data compression
Efficiency
Fractals
Image compression
Image quality
Information technology
International conferences
International organizations
Methods
Multimedia
Signal processing
Similarity
Storage
Time compression
Video compression
Video data
Video transmission
ISSN:1076-2787, 1099-0526
Online Access:Get full text
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Summary: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.
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ISSN:1076-2787
1099-0526
DOI:10.1155/2018/2016976