Abstract
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.
Highlights
With the development of high definition videos such as digital broadcasting and mobile video, video services are quickly evolving with the popularity of the internet and mobile networks
In order to solve this problem, the high efficiency video coding (HEVC) standard is developed by the Joint Collaborate Team on Video Coding (JCT-VC) which consists of the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG)[1]
In this paper, the intra features and inter features related to fast decision for coding unit (CU), prediction unit (PU) and transformation unit (TU) are exploited
Summary
With the development of high definition videos such as digital broadcasting and mobile video, video services are quickly evolving with the popularity of the internet and mobile networks. A fast coding algorithm by early mode selection based on the intra block similarity is presented in [2]. A fast inter-mode decision algorithm by jointly using the correlations of PU, motion vector and RD cost at different CU depth is proposed in [17]. Some fast coding algorithms involved with machine learning are published In these algorithms, block partition and mode selection in video coding are modeled as data classification via online learning or offline learning. 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.
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