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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2022.3226417
|2 doi
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|a pubmed24n1183.xml
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|a (DE-627)NLM355202700
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|a (NLM)37015524
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Yang, Li
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Blind VQA on 360° Video via Progressively Learning from Pixels, Frames and Video
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 04.04.2023
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Blind visual quality assessment (BVQA) on 360° video plays a key role in optimizing immersive multimedia systems. When assessing the quality of 360° 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° 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° 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° 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° video over two datasets, the code of which has been public in https://github.com/yanglixiaoshen/ProVQA
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Xu, Mai
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Shengxi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Guo, Yichen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Zulin
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g PP(2022) vom: 07. Dez.
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
8 |
|g volume:PP
|g year:2022
|g day:07
|g month:12
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2022.3226417
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