Fast intra coding based on online learning for high efficiency video coding

Lu, Yu; Liu, Huaping; Lin, Yameng; Yao, Yingbiao; Yin, Haibing

doi:10.1016/j.ijleo.2018.04.044

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…Again in [24], a fuzzy SVM for fast CU decision algorithm based on RD cost optimization is proposed, where CU partition is regarded as a cascaded process of multi-level classification. In our previous work [25], CU depth is early decided by the CU partition threshold which is acquired by online learning the random sample set. One merit of machine learning based algorithm is that coding features can be acquired by adequately exploiting the massive coding data by the learning tool, which is more beneficial to fast coding.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Classification for Complexity Reduction in HEVC Inter Coding

Huang

Liu

et al. 2020

IEEE Access

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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.

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Section: Introductionmentioning

confidence: 99%