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10 October 2020 Panoramic video assessment based on cascaded network using saliency map
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Conference Poster
Virtual reality (VR) refers to a technology that allows people to experience the virtual world in an artificial environment. As one of the most important forms of VR media content, panoramic video can provide viewers with 360-degree free viewing angles. However, the acquisition, stitching, transmission and playback of panoramic video may damage the video quality and seriously affect the viewer's quality of experience. Therefore, how to improve the display quality and provide users with a better visual experience has become a hot topic in this field. When watching the videos, people pay attention to the salient areas, especially for the panoramic videos that people can choose the regions of interest freely. Considering this characteristic, the saliency information needs to be utilized when performing quality assessment. In this paper, we use two cascaded networks to calculate the quality score of panoramic video without reference video. First, the saliency prediction network is used to compute the saliency map of the image, and the patches with higher saliency are selected through the saliency map. In this way, we can exclude the areas in the panoramic image that have no positive effect on the quality assessment task. Then, we input the selected small salient patches into the quality assessment network for prediction, and obtain the final image quality score. Experimental results show that the proposed method can achieve more accurate quality scores for the panoramic videos compared with the state-of-the-art works due to its special network structure.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wenxin Ding, Ping An, Chao Yang, and Xinpeng Huang "Panoramic video assessment based on cascaded network using saliency map", Proc. SPIE 11550, Optoelectronic Imaging and Multimedia Technology VII, 115501A (10 October 2020);


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