Content-Adaptive Feature-Based CU Size Prediction for Fast Low-Delay Video Encoding in HEVC
Kodikara Arachchi, Hemantha
Institute of Electrical and Electronics Engineers (IEEE)
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Determining the best partitioning structure of a coding tree unit is one of the most time-consuming operations in High Efficiency Video Coding (HEVC) encoding. Specifically, it is the evaluation of the quadtree hierarchy using the rate-distortion (RD) optimization that has the most significant impact on the encoding time, especially in the cases of high definition (HD) and ultra HD videos. In order to expedite the encoding for low-delay applications, this paper proposes a coding unit (CU) size selection and encoding algorithm for inter prediction in the HEVC. To this end, it describes: 1) two CU classification models based on Inter N × N mode motion features and RD cost thresholds to predict the CU split decision; 2) an online training scheme for dynamic content adaptation; 3) a motion vector reuse mechanism to expedite the motion estimation process; and 4) finally introduces a computational complexity to coding efficiency tradeoff process to enable flexible control of the algorithm. The experimental results reveal that the proposed algorithm achieves a consistent average encoding time performance ranging from 55%-58% and 57%-61% with average Bjøntegaard delta bit rate increases of 1.93%-2.26% and 2.14%-2.33% compared with the HEVC 16.0 reference software for the low delay P and low delay B configurations, respectively, across a wide range of content types and bit rates.
IEEE Transactions on Circuits and Systems for Video Technology;
Mallikarachchi, T., Talagala, D., Kodikara Arachchi, H. and Fernando, A. (2016) 'Content-Adaptive Feature-Based CU Size Prediction for Fast Low-Delay Video Encoding in HEVC', IEEE Transactions on Circuits and Systems for Video Technology, 28 (3). pp. 693-705. DOI: 10.1109/TCSVT.2016.2619499.
Article published in IEEE Transactions on Circuits and Systems for Video Technology on 20 October 2016, available at: https://doi.org/10.1109/TCSVT.2016.2619499.
Sponsored by: IEEE Circuits and Systems Society;
Funding: 10.13039/501100000780-ACTION-TV Project, which is funded under European Commission’s 7th Framework Program
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