• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Fast Algorithm for Intra Prediction of HEVC Using Adaptive Decision Trees

Vol. 10, No. 7, July 30, 2016
10.3837/tiis.2016.07.023, Download Paper (Free):

Abstract

High Efficiency Video Coding (HEVC) Standard, as the latest coding standard, introduces satisfying compression structures with respect to its predecessor Advanced Video Coding (H.264/AVC). The new coding standard can offer improved encoding performance compared with H.264/AVC. However, it also leads to enormous computational complexity that makes it considerably difficult to be implemented in real time application. In this paper, based on machine learning, a fast partitioning method is proposed, which can search for the best splitting structures for Intra-Prediction. In view of the video texture characteristics, we choose the entropy of Gray-Scale Difference Statistics (GDS) and the minimum of Sum of Absolute Transformed Difference (SATD) as two important features, which can make a balance between the computation complexity and classification performance. According to the selected features, adaptive decision trees can be built for the Coding Units (CU) with different size by offline training. Furthermore, by this way, the partition of CUs can be resolved as a binary classification problem. Experimental results have shown that the proposed algorithm can save over 34% encoding time on average, with a negligible Bjontegaard Delta (BD)-rate increase.


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Cite this article

[IEEE Style]
X. Zheng, Y. Zhao, H. Bai and C. Lin, "Fast Algorithm for Intra Prediction of HEVC Using Adaptive Decision Trees," KSII Transactions on Internet and Information Systems, vol. 10, no. 7, pp. 3286-3300, 2016. DOI: 10.3837/tiis.2016.07.023.

[ACM Style]
Xing Zheng, Yao Zhao, Huihui Bai, and Chunyu Lin. 2016. Fast Algorithm for Intra Prediction of HEVC Using Adaptive Decision Trees. KSII Transactions on Internet and Information Systems, 10, 7, (2016), 3286-3300. DOI: 10.3837/tiis.2016.07.023.