Hierarchical Classification for Complexity Reduction in HEVC Inter Coding

doi:10.1109/ACCESS.2020.2977422

Hierarchical Classification for Complexity Reduction in HEVC Inter Coding

2020-01-01

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DOI：10.1109/ACCESS.2020.2977422

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In order to adapt to various real-time applications, fast coding algorithms for high efficiency video coding (HEVC) standard maintain a hot research topic in recent years. In this paper, a complexity reduction algorithm based on hierarchical classification for HEVC inter coding is proposed. It consists of five fast algorithms which is accomplished by hierarchical classification trees at coding unit (CU) level, prediction unit (PU) level and transformation unit (TU) level respectively. At the beginning of proposed algorithm, intra features and inter features which describe the texture and context properties of CU, PU and TU are extracted from the training set. Then the classification trees for CU, PU and TU are generated by carefully selecting features and designing the classification criteria. By analyzing the spatiotemporal correlation, two strategies including early termination and early split are applied to fast coding by referring to these classification trees. The objective evaluation demonstrates that the proposed algorithm can significantly reduce coding complexity with little compression loss. Particularly the subjective evaluation based on similarity measurement for color histogram approves that decoded video quality between the original HM16.9 algorithm and the proposed algorithm is nearly identical.

Keywords:

Encoding

Classification algorithms

Prediction algorithms

Partitioning algorithms

Correlation

Complexity theory

Machine learning algorithms

HEVC

inter coding

fast coding

coding unit

prediction unit

transformation unit

classification tree

histogram similarity

Hierarchical Classification for Complexity Reduction in HEVC Inter Coding

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