Content-adaptive feature-based CU size prediction for fast low-delay video encoding in HEVC

Mallikarachchi, T., Talagala, D.S., Arachchi, H.K. ORCID: 0000-0002-5631-3239 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. ISSN 1051-8215

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Abstract

Determining the best partitioning structure of a Coding Tree Unit (CTU) is one of the most time consuming operations in 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 High Definition (UHD) 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 (i) two CU classification models based on Inter N×N mode motion features and RD cost thresholds to predict the CU split decision, (ii) an online training scheme for dynamic content adaptation, (iii) a motion vector reuse mechanism to expedite the motion estimation process, and finally introduces (iv) a computational complexity to coding efficiency trade-off 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 (BDBR) increases of 1.93% –
2.26% and 2.14% – 2.33% compared to 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.

Item Type: Journal article
Publication Title: IEEE Transactions on Circuits and Systems for Video Technology
Creators: Mallikarachchi, T., Talagala, D.S., Arachchi, H.K. and Fernando, A.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 20 October 2016
ISSN: 1051-8215
Identifiers:
NumberType
10.1109/TCSVT.2016.2619499DOI
Divisions: Schools > School of Science and Technology
Depositing User: Linda Sullivan
Date Added: 05 Sep 2017 15:33
Last Modified: 06 Sep 2017 08:22
URI: http://irep.ntu.ac.uk/id/eprint/31550

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