FBR-CNN: A Feedback Recurrent Network for Video Saliency Detection

Author(s):  
Guanqun Ding ◽  
Nevrez Imamoglu ◽  
Ali Caglayan ◽  
Masahiro Murakawa ◽  
Ryosuke Nakamura
2015 ◽  
Vol 38 ◽  
pp. 32-44 ◽  
Author(s):  
Qin Tu ◽  
Aidong Men ◽  
Zhuqing Jiang ◽  
Feng Ye ◽  
Jun Xu

2018 ◽  
Vol 20 (11) ◽  
pp. 2993-3007 ◽  
Author(s):  
Xiaofei Zhou ◽  
Zhi Liu ◽  
Chen Gong ◽  
Wei Liu

Author(s):  
Hongfa Wen ◽  
Xiaofei Zhou ◽  
Yaoqi Sun ◽  
Jiyong Zhang ◽  
Chenggang Yan

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 165 ◽  
Author(s):  
Xiantao Jiang ◽  
Tian Song ◽  
Daqi Zhu ◽  
Takafumi Katayama ◽  
Lu Wang

Perceptual video coding (PVC) can provide a lower bitrate with the same visual quality compared with traditional H.265/high efficiency video coding (HEVC). In this work, a novel H.265/HEVC-compliant PVC framework is proposed based on the video saliency model. Firstly, both an effective and efficient spatiotemporal saliency model is used to generate a video saliency map. Secondly, a perceptual coding scheme is developed based on the saliency map. A saliency-based quantization control algorithm is proposed to reduce the bitrate. Finally, the simulation results demonstrate that the proposed perceptual coding scheme shows its superiority in objective and subjective tests, achieving up to a 9.46% bitrate reduction with negligible subjective and objective quality loss. The advantage of the proposed method is the high quality adapted for a high-definition video application.


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