Acceleration of Kvazaar HEVC Intra Encoder With Machine Learning

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complexity of High Efficiency Video Coding (HEVC) poses a real challenge to HEVC encoder implementations. Particularly, the complexity stems from the HEVC quad-tree structure that also has an integral part in HEVC coding efficiency. This paper presents a Machine Learning (ML) based technique for pruning the HEVC quad-tree without deteriorating coding gain. We show how ML decision trees can be used to predict a depth interval for a quad-tree before the Rate-Distortion Optimization (RDO). This approach limits the number of RDO candidates and thus speeds up encoding. The proposed technique works particularly well with high-quality video coding and it is shown to accelerate the veryslow preset of practical Kvazaar HEVC intra encoder by 1.35× with 0.49% bit rate increase. Compared with the corresponding preset of ×265 encoder, Kvazaar is 2.12× as fast at a cost of under 1.21% bit rate overhead. These results indicate that the optimized Kvazaar is the leading open-source encoder in high-quality HEVC intra coding.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing (ICIP)
Number of pages5
ISBN (Electronic)978-1-5386-6249-6
ISBN (Print)978-1-5386-6250-2
Publication statusPublished - 26 Aug 2019
Publication typeA4 Article in conference proceedings
EventIEEE International Conference on Image Processing -
Duration: 1 Jan 1900 → …

Publication series

NameIEEE International Conference on Image Processing
ISSN (Print)1522-4880
ISSN (Electronic)2381-8549


ConferenceIEEE International Conference on Image Processing
Period1/01/00 → …

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  • Publication forum level 1


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