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Volume 38 Issue 3
Mar.  2016
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WU Xianyun, GAO Yuanyuan, LIU Kai, LI Yunsong. An Optimized Inter-prediction Algorithm for High Efficiency Video Coding Based on Texture Similarity[J]. Journal of Electronics & Information Technology, 2016, 38(3): 655-660. doi: 10.11999/JEIT150672
Citation: WU Xianyun, GAO Yuanyuan, LIU Kai, LI Yunsong. An Optimized Inter-prediction Algorithm for High Efficiency Video Coding Based on Texture Similarity[J]. Journal of Electronics & Information Technology, 2016, 38(3): 655-660. doi: 10.11999/JEIT150672

An Optimized Inter-prediction Algorithm for High Efficiency Video Coding Based on Texture Similarity

doi: 10.11999/JEIT150672
Funds:

China Postdoctoral Science Foundation (2013M540735), The National Natural Science Foundation of China (61301291, 61301287, 61222101), 111 Project (B08038), Shaanxi Province Science and Technology Innovation Team, Central University Basic Research Fund

  • Received Date: 2015-06-04
  • Rev Recd Date: 2015-11-25
  • Publish Date: 2016-03-19
  • In this paper, an optimized inter-prediction algorithm is proposed for High Efficiency Video Coding (HEVC) based on video texture similarity. With the increasing of the video resolution, spatial statistical redundancy is increased meanwhile. HEVC improves the coding efficiency by using large coding units, therefore increases the coding complexity significantly. For video sequence, the flat area and texture area are high correlated between adjacent frames. In this paper, previous encoded depth or split depth information is used to determine fast the depth of current encoding coding unit. For flat area, the algorithm predicts the maximum depth to reduce the mode decision for smaller coding units. For texture area, the algorithm can fast determine the minimum depth which can save the mode decision for larger coding units. Experimental result shows that the proposed algorithm can reduce 50% coding complexity compared with original HEVC algorithm. Meanwhile, the average PSNR only decreases 0.09 dB and the average coding rate is increased about 0.13%.
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