Effective Intra Mode Prediction of 3D-HEVC System based on Big Data Clustering and Data Mining

doi:10.23940/ijpe.19.12.p13.32193226

Abstract

Abstract: 3D-high efficiency video coding (3D-HEVC) structure is a development of HEVC, but some new coding techniques are added on the basis of it to make it more conducive to encoding depth maps and multi-view. In 3D-HEVC, the intra prediction mode decision for depth level contains closed connections with coding unit (CU) partition. This process, quad-tree block splitting, gives the gorgeous coding efficiency to 3D-HEVC, but it incurs unacceptable computational burdens because each possible coding mode is tested to rank the most suitable one. According to previous works, whether the current CU will be divided into smaller sizes is dependent on encoding contexts. In view of that, this paper proposed a novel method to speed up intra coding unit splitting, relying on data clustering and data mining. The experimental results showed that our new approach can reach a satisfied balance between computational burdens and RD cost.

Key words: 3D-HEVC system, data clustering, data mining

Jinchao Zhao, Shuaichao Wei, and Qiuwen Zhang. Effective Intra Mode Prediction of 3D-HEVC System based on Big Data Clustering and Data Mining [J]. Int J Performability Eng, 2019, 15(12): 3219-3226.

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