Blind VQA on 360{\deg} Video via Progressively Learning from Pixels, Frames and Video

• URL: http://arxiv.org/abs/2111.09503v1
• Date: Thu, 18 Nov 2021 03:45:13 GMT
• Title: Blind VQA on 360{\deg} Video via Progressively Learning from Pixels, Frames and Video
• Authors: Li Yang, Mai Xu, Shengxi Li, Yichen Guo, Zulin Wang
• Abstract summary: Blind visual quality assessment (BVQA) on 360textdegree video plays a key role in optimizing immersive multimedia systems. In this paper, we take into account the progressive paradigm of human perception towards spherical video quality. We propose a novel BVQA approach (namely ProVQA) for 360textdegree video via progressively learning from pixels, frames and video.
• Score: 66.57045901742922
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Blind visual quality assessment (BVQA) on 360{\textdegree} video plays a key role in optimizing immersive multimedia systems. When assessing the quality of 360{\textdegree} video, human tends to perceive its quality degradation from the viewport-based spatial distortion of each spherical frame to motion artifact across adjacent frames, ending with the video-level quality score, i.e., a progressive quality assessment paradigm. However, the existing BVQA approaches for 360{\textdegree} video neglect this paradigm. In this paper, we take into account the progressive paradigm of human perception towards spherical video quality, and thus propose a novel BVQA approach (namely ProVQA) for 360{\textdegree} video via progressively learning from pixels, frames and video. Corresponding to the progressive learning of pixels, frames and video, three sub-nets are designed in our ProVQA approach, i.e., the spherical perception aware quality prediction (SPAQ), motion perception aware quality prediction (MPAQ) and multi-frame temporal non-local (MFTN) sub-nets. The SPAQ sub-net first models the spatial quality degradation based on spherical perception mechanism of human. Then, by exploiting motion cues across adjacent frames, the MPAQ sub-net properly incorporates motion contextual information for quality assessment on 360{\textdegree} video. Finally, the MFTN sub-net aggregates multi-frame quality degradation to yield the final quality score, via exploring long-term quality correlation from multiple frames. The experiments validate that our approach significantly advances the state-of-the-art BVQA performance on 360{\textdegree} video over two datasets, the code of which has been public in \url{https://github.com/yanglixiaoshen/ProVQA.}

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