Paper Info
Title
Fast inter-frame prediction in multi-view video coding based on perceptual distortion threshold model.
Abstract
Multi-view video coding (MVC) utilizes hierarchical B picture prediction structure and adopts many coding techniques to remove spatiotemporal and inter-view redundancies at the cost of high computational complexity. In this paper, a novel perceptual distortion threshold model (PDTM) is proposed to reveal the relationship between the mode selection of inter-frame prediction and coding distortion threshold. Based on the proposed PDTM, a new fast inter-frame prediction algorithm in MVC is developed aimed at minimizing computational complexity for dependent view coding. Then the fast MVC algorithm is incorporated into the multi-view High Efficiency Video Coding (MV-HEVC) software to improve MVC coding efficiency. In practical coding, the mode selection for inter-frame prediction of dependent views may be early terminated based on the thresholds derived from the PDTM, thereby reducing the coding time complexity. Experimental results demonstrate that the proposed algorithm can reduce the computational complexity of the dependent views by 52.9% compared with the HTM14.1 algorithm under the coding structure of hierarchical B pictures. Moreover, the bitrate is increased by 0.9% under the same subjective quality and only increased by 1.0% under the same objective quality peak signal-to-noise ratio (PSNR). Compared with the state-of-the-art fast algorithm, the proposed algorithm can save more coding time, while the bitrate under the same PSNR increases slightly.
Year
DOI
Venue
2019
10.1016/j.image.2018.10.002
Signal Processing: Image Communication
Keywords
Field
DocType
Multi-view video coding,Perceptual distortion threshold model,Binocular just noticeable difference,Fast mode decision
Computer vision,Algorithmic efficiency,Computer science,Perceptual Distortion,Algorithm,Coding (social sciences),Inter frame,Artificial intelligence,Time complexity,Threshold model,Video compression picture types,Computational complexity theory
Journal
Volume
ISSN
Citations 
70
0923-5965
0
PageRank 
References 
Authors
0.34
24
7
Name
Order
Citations
PageRank
Gangyi Jiang16111.75
Baozhen Du201.01
Shuqing Fang300.34
Mei Yu454286.20
Feng Shao560372.75
Zongju Peng627657.69
Fen Chen78120.55