scholarly journals Fast Intra Mode Decision in High Efficiency Video Coding

Polibits ◽  
2014 ◽  
Vol 50 ◽  
pp. 5-12 ◽  
Author(s):  
H. Brahmasury Jain ◽  
K.R. Rao
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Mode Decision ◽  
High Efficiency Video Coding ◽  
Intra Mode Decision ◽  
Fast Intra Mode Decision

scholarly journals Fast Intra Mode Decision Algorithm for HEVC Based on Block Textural Properties

2020 ◽  
pp. short57-1-short57-8
Author(s):  
Ban Doan ◽  
Andrey Tropchenko
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Textural Properties ◽  
Mode Decision ◽  
Depth Range ◽  
High Efficiency Video Coding ◽  
Decision Algorithm ◽  
Coding Efficiency ◽  
Intra Mode Decision ◽  
Fast Intra Mode Decision

In order to achieve greater coding efficiency compared with the previous video coding standards, various advanced coding techniques are used in the High Efficiency Video Coding (HEVC) standard, such as a flexible partition and a large number of intra prediction modes. However, these techniques lead to much greater complexity that restricts HEVC from realtime applications. To solve this problem, a fast intra mode decision algorithm is proposed in this paper that uses the block’s textural properties to determine the partition depth range and decide whether to split or skip smaller sizes of the coding unit. Besides that, the number of candidate modes for the rough mode decision process is also reduced depending on the block’s property. Experimental results for the recommended test sequences by the JCT-VC show that the proposed algorithm can save an average of 44% encoder time with a slight loss in performance compared to the reference software HM-16.20.

Machine Learning-Based Coding Decision Making in H.265/HEVC CTU Division and Intra Prediction

2020 ◽  
Vol 11 (2) ◽  
pp. 41-60
Author(s):  
Wenchan Jiang ◽  
Ming Yang ◽  
Ying Xie ◽  
Zhigang Li
Keyword(s):  
Machine Learning ◽  
Video Coding ◽  
High Efficiency ◽  
Main Tool ◽  
Decision Time ◽  
Mode Decision ◽  
Supervised Machine Learning ◽  
High Efficiency Video Coding ◽  
Neuron Network ◽  
Prediction Mode

High efficiency video coding (HEVC) has been deemed as the newest video coding standard of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group. In this research project, in compliance with H.265 standard, the authors focused on improving the performance of encode/decode by optimizing the partition of prediction block in coding unit with the help of supervised machine learning. The authors used Keras library as the main tool to implement the experiments. Key parameters were tuned for the model in the convolution neuron network. The coding tree unit mode decision time produced in the model was compared with that produced in the reference software for HEVC, and it was proven to have improved significantly. The intra-picture prediction mode decision was also investigated with modified model and yielded satisfactory results.

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