Low-complexity saliency detection algorithm for fast perceptual video coding.
A low-complexity saliency detection algorithm for perceptual video coding is proposed; low-level encoding information is adopted as the characteristics of visual perception analysis. Firstly, this algorithm employs motion vector (MV) to extract temporal saliency region through fast MV noise filtering and translational MV checking procedure. Secondly, spatial saliency region is detected based on optimal prediction mode distributions in I-frame and P-frame. Then, it combines the spatiotemporal saliency detection results to define the video region of interest (VROI). The simulation results validate that the proposed algorithm can avoid a large amount of computation work in the visual perception characteristics analysis processing compared with other existing algorithms; it also has better performance in saliency detection for videos and can realize fast saliency detection. It can be used as a part of the video standard codec at medium-to-low bit-rates or combined with other algorithms in fast video coding.
School of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China.
Pengyu
P
Liu
School of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China.
Kebin
K
Jia
D000465:Algorithms; D006801:Humans; D008962:Models, Theoretical; D014741:Video Recording / Q000379:methods*