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20231229124305.0 |
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231229s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2023.3345227
|2 doi
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|a eng
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| 100 |
1 |
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|a Venkataramanan, Abhinau K
|e verfasserin
|4 aut
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| 245 |
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|a One Transform To Compute Them All
|b Efficient Fusion-Based Full-Reference Video Quality Assessment
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|c 2023
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 28.12.2023
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a The Visual Multimethod Assessment Fusion (VMAF) algorithm has recently emerged as a state-of-the-art approach to video quality prediction, that now pervades the streaming and social media industry. However, since VMAF requires the evaluation of a heterogeneous set of quality models, it is computationally expensive. Given other advances in hardware-accelerated encoding, quality assessment is emerging as a significant bottleneck in video compression pipelines. Towards alleviating this burden, we propose a novel Fusion of Unified Quality Evaluators (FUNQUE) framework, by enabling computation sharing and by using a transform that is sensitive to visual perception to boost accuracy. Further, we expand the FUNQUE framework to define a collection of improved low-complexity fused-feature models that advance the state-of-the-art of video quality performance with respect to both accuracy, by 4.2% to 5.3%, and computational efficiency, by factors of 3.8 to 11 times!
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|a Journal Article
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|a Stejerean, Cosmin
|e verfasserin
|4 aut
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|a Katsavounidis, Ioannis
|e verfasserin
|4 aut
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| 700 |
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|a Bovik, Alan C
|e verfasserin
|4 aut
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| 773 |
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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(2023) vom: 27. Dez.
|w (DE-627)NLM09821456X
|x 1941-0042
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|g month:12
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|u http://dx.doi.org/10.1109/TIP.2023.3345227
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