News

 Statistics

Number of Journals34
Number of Issues1,306
Number of Articles9,428
Article View9,191,886
PDF Download5,622,554
Farsi, H., Naseri, A., Etezadifar, P. (2019). Robustness of Compressed Video in H.264 Against Channel Using Neural Network with Huffman Coding. Electronic and Cyber Defense, 6(4), 221-234.
Hassan Farsi; Ali Naseri; pouryia Etezadifar. "Robustness of Compressed Video in H.264 Against Channel Using Neural Network with Huffman Coding". Electronic and Cyber Defense, 6, 4, 2019, 221-234.
Farsi, H., Naseri, A., Etezadifar, P. (2019). 'Robustness of Compressed Video in H.264 Against Channel Using Neural Network with Huffman Coding', Electronic and Cyber Defense, 6(4), pp. 221-234.
Farsi, H., Naseri, A., Etezadifar, P. Robustness of Compressed Video in H.264 Against Channel Using Neural Network with Huffman Coding. Electronic and Cyber Defense, 2019; 6(4): 221-234.

Robustness of Compressed Video in H.264 Against Channel Using Neural Network with Huffman Coding

علوم و فناوریهای پدافند نوین
Article 1, Volume 6, Issue 4 - Serial Number 22, March 2016, Pages 221-234    PDF (822.34 K)
Authors
Hassan Farsi* ; Ali Naseri; pouryia Etezadifar
Receive Date: 30 January 2019,  Revise Date: 17 July 2025,  Accept Date: 30 January 2019 
Abstract
As source and channel coding are performed independently from each other without any feedback, source coding tries to remove redundancy of the information whereas channel coding tries to increase reliability of transmitted data. As known, channel capacity restricts volume of the transmitted data and so depending to conditions, it is required to have a tradeoff between source and channel coding. The aim of this paper is to improve the quality of the synthesized video by increasing the robustness against channel errors in fixed transmission rate. In other words, the robustness of the transmitted video frames increases without any increment in bit rate. This results in improvement in the quality of the synthesized video. In the proposed method, the transmitted information is considerably compressed using neural network with Huffman coding in H. 264. Then a secondary channel coding whose rate depends on the amount of the compression is applied on the compressed information. This causes that the proposed method is able to increase channel coding rate and therefore provides higher protection for the transmitted information and more robustness against channel errors. The obtained results by the proposed method are compared to the other methods for different source coding rates and SNRs.
Keywords
Video Compression; H. 264; Variable Bit Rate; Channel Coding; Adaptive Source and Channel Coding; artificial neural network
Statistics
Article View: 1,105
PDF Download: 254