Results 21 to 30 of about 492 (173)
PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wild
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Video quality assessment (VQA) is a challenging problem due to the numerous factors that can affect the perceptual quality of a video, \eg, content attractiveness, distortion type, motion pattern, and level. However, annotating the Mean opinion score (MOS) for videos is expensive and time-consuming, which limits the scale of VQA datasets, and poses a ...
Kun Yuan 0003 +8 more
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Objective Underwater Video Quality Assessment Model via Two-Stream Networks [PDF]
Jisuanji kexue yu tansuo, 2023 Underwater videos often suffer from quality degradation. On the one hand, the exponential attenuation of the natural light in water media leads to the loss of underwater video quality.
SONG Wei, XIAO Yi, DU Yanling, ZHANG Minghua
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IEEE Transactions on Circuits and Systems for Video TechnologyInspired by the dual-stream theory of the human visual system (HVS) - where the ventral stream is responsible for object recognition and detail analysis, while the dorsal stream focuses on spatial relationships and motion perception - an increasing number of video quality assessment (VQA) works built upon this framework are proposed.
Li Yu +3 more
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Deep attributes and decisions fusion for no-reference video quality analysis [PDF]
Big Data and Computing Visions, 2023 Video Quality Assessment (VQA) is a critical component of various technologies, including automated video broadcasting through displaying technologies.
Adil Baig
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Review of Research on Video Quality Assessment Based on Deep Learning
Jisuanji kexue yu tansuo, 2021 Video quality assessment (VQA) is based on the subjective quality assessment results of the human eye, using models to evaluate distorted videos. It is difficult for traditional assessment methods to make subjective assessment results consistent with ...
TAN Yaya, KONG Guangqian
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No-Reference Video Quality Assessment Using Distortion Learning and Temporal Attention
IEEE Access, 2022 The rapid growth of video consumption and multimedia applications has increased the interest of the academia and industry in building tools that can evaluate perceptual video quality. Since videos might be distorted when they are captured or transmitted,
Koffi Kossi +3 more
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Sensors, 2022 With the constantly growing popularity of video-based services and applications, no-reference video quality assessment (NR-VQA) has become a very hot research topic.
Domonkos Varga
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Video quality assessment using motion-compensated temporal filtering and manifold feature similarity. [PDF]
PLoS ONE, 2017 Well-performed Video quality assessment (VQA) method should be consistent with human visual systems for better prediction accuracy. In this paper, we propose a VQA method using motion-compensated temporal filtering (MCTF) and manifold feature similarity.
Yang Song +4 more
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MRET: Multi-resolution transformer for video quality assessment
Frontiers in Signal Processing, 2023 No-reference video quality assessment (NR-VQA) for user generated content (UGC) is crucial for understanding and improving visual experience. Unlike video recognition tasks, VQA tasks are sensitive to changes in input resolution.
Junjie Ke +4 more
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Quality Feature Learning via Multi-Channel CNN and GRU for No-Reference Video Quality Assessment
IEEE Access, 2023 Nowadays, video quality assessment (VQA) plays a vital role in video-related industries to predict human perceived video quality to maintain the quality of service.
Ngai-Wing Kwong +3 more
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