Title
Perceptual feature guided rate distortion optimization for high efficiency video coding
Abstract
With the advances in understanding perceptual properties of the human visual system, perceptual video coding, which aims to incorporate human perceptual mechanisms into video coding for maximizing the perceptual coding efficiency, becomes an essential research topic. Since the newest video coding standard—high efficiency video coding (HEVC) does not fully consider the perceptual characteristic of the input video, a perceptual feature guided rate distortion optimization (RDO) method is presented to improve its perceptual coding performance in this paper. In the proposed method, for each coding tree unit, the spatial perceptual feature (i.e., gradient magnitude ratio) and the temporal perceptual feature (i.e., gradient magnitude similarity deviation ratio) are extracted by considering the spatial and temporal perceptual correlations. These perceptual features are then utilized to guide the RDO process by perceptually adjusting the corresponding Lagrangian multiplier. By incorporating the proposed method into the HEVC, extensive simulation results have demonstrated that the proposed approach can significantly improve the perceptual coding performance and obtain better visual quality of the reconstructed video, compared with the original RDO in HEVC.
Year
DOI
Venue
2017
https://doi.org/10.1007/s11045-016-0395-2
Multidim. Syst. Sign. Process.
Keywords
Field
DocType
Human visual system,High efficiency video coding,Perceptual feature,Rate distortion optimization
Coding tree unit,Pattern recognition,Human visual system model,Computer science,Lagrange multiplier,Perceptual coding,Speech recognition,Coding (social sciences),Gradient magnitude,Artificial intelligence,Perception,Rate–distortion optimization
Journal
Volume
Issue
ISSN
28
4
0923-6082
Citations 
PageRank 
References 
4
0.39
18
Authors
5
Name
Order
Citations
PageRank
Aisheng Yang140.39
Huanqiang Zeng239536.94
Jing Chen38810.64
Jianqing Zhu47810.10
Canhui Cai533927.80