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2013 (Vol. 6, Issue: 17)
Article Information:

Quality Enhancement of Synthesized Video by Improvement of VLC Using Artificial Neural Network

Hassan Farsi and Pouriya Etezadifar
Corresponding Author:  Hassan Farsi 

Key words:  Artificial neural network, channel coding, Huffman coding, variable bit rate, Variable Length Coding (VLC), video coding,
Vol. 6 , (17): 3098-3109
Submitted Accepted Published
December 12, 2012 January 21, 2013 September 20, 2013
Abstract:

Progress of technology in recent decade’s causes that video transmission via communication channels has met high demands. Therefore, several methods have been proposed to improve the quality of video under channel errors. The aim of this study is to increase PSNR for synthesized video by increasing channel encoder rate but in constant transmission rate. This is achieved by using intelligent neural network and Huffman coding in VLC blocks used in the MPEG standard to compress transmitted data significantly. Then, depending to the amount of compression by the proposed method, the compressed data is coded again using secondary channel encoder. The proposed method is able to increase channel coding rate without increasing the amount of information for each frame. This method provides more robustness for video frames against channel errors. The proposed method is tested for different source coding rates and several SNRs for channel and the obtained results are compared with a new method named Farooq method.
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  Cite this Reference:
Hassan Farsi and Pouriya Etezadifar, 2013. Quality Enhancement of Synthesized Video by Improvement of VLC Using Artificial Neural Network.  Research Journal of Applied Sciences, Engineering and Technology, 6(17): 3098-3109.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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