Acceleration of Kvazaar HEVC Intra Encoder With Machine Learning
Mercat, Alexandre; Lemmetti, Ari; Viitanen, Marko; Vanne, Jarno (2019-08-26)
Mercat, Alexandre
Lemmetti, Ari
Viitanen, Marko
Vanne, Jarno
IEEE
26.08.2019
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-201911145962
https://urn.fi/URN:NBN:fi:tuni-201911145962
Kuvaus
Peer reviewed
Tiivistelmä
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.
Kokoelmat
- TUNICRIS-julkaisut [16983]