Joong Gon Yim
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Video quality assessment for User Generated Content (UGC) is an important topic in both industry and academia. Most existing methods only focus on one aspect of the perceptual quality assessment, such as technical quality or compression artifacts. In this paper, we create a large scale dataset to comprehensively investigate characteristics of generic UGC video quality. Besides the subjective ratings and content labels of the dataset, we also propose a DNN-based framework to thoroughly analyze importance of content, technical quality, and compression level in perceptual quality. Our model is able to provide quality scores as well as human-friendly quality indicators, to bridge the gap between low level video signals to human perceptual quality. Experimental results show that our model achieves state-of-the-art correlation with Mean Opinion Scores (MOS).
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Subjective Quality Assessment for YouTube UGC Dataset
2020 IEEE International Conference on Image Processing
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User Generated Contents~(UGC) received a lot of interests in academia and industry recently. To facilitate compression-related research on UGC, YouTube has released a large scale dataset~\cite{Wang2019UGCDataset}. The initial dataset only provided raw videos, which made it difficult for quality assessment. In this paper, we built a crowd-sourcing platform to collect and cleanup subjective quality scores for YouTube UGC dataset, and analyzed the distribution of Mean Opinion Score (MOS) in various dimensions. Some fundamental question in video quality assessment are also investigated, like the correlation between full video MOS and corresponding chunk MOS, and the influence of chunk variation in quality score aggregation.
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Video transcoding optimization based on input perceptual quality
SPIE Optical Engineering + Applications: Applications of Digital Image Processing XLIII (2020)
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Todays video transcoding pipelines choose transcoding parameters based on Rate-Distortion curves, which mainlyfocuses on the relative quality difference between original and transcoded videos. By investigating recentlyreleased YouTube UGC dataset, we found that people were more tolerant to the quality changes in low qualityinputs than in high quality inputs, which suggests that current transcoding framework could be further optimizedby considering input perceptual quality. An efficient machine learning based metric was proposed to detect lowquality inputs, whose bitrate can be further reduced without hurting perceptual quality. To evaluate the impacton perceptual quality, we conducted a crowd-sourcing subjective experiment, and provided a methodology toevaluate statistical significance among different treatments. The results showed that the proposed quality guidedtranscoding framework is able to reduce the average bitrate upto 5% with insignificant quality degradation.
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