No-reference (NR) perceptual video quality assessment (VQA) is a complex,
unsolved, and important problem to social and streaming media applications.
Efficient and accurate video quality predictors are needed to monitor and guide
the processing of billions of shared, often imperfect, user-generated content
(UGC). Unfortunately, current NR models are limited in their prediction
capabilities on real-world, "in-the-wild" UGC video data. To advance progress
on this problem, we created the largest (by far) subjective video quality
dataset, containing 39, 000 realworld distorted videos and 117, 000 space-time
localized video patches ('v-patches'), and 5.5M human perceptual quality
annotations. Using this, we created two unique NR-VQA models: (a) a
local-to-global region-based NR VQA architecture (called PVQ) that learns to
predict global video quality and achieves state-of-the-art performance on 3 UGC
datasets, and (b) a first-of-a-kind space-time video quality mapping engine
(called PVQ Mapper) that helps localize and visualize perceptual distortions in
space and time. We will make the new database and prediction models available
immediately following the review process.

该研究着重探讨了无参考视觉质量评估（NR VQA）问题，研究创建了包含 39,000 个失真视频和 117,000 个空时定位视频片段的最大人类感知质量评价数据库，以及两种独特的 NR-VQA 模型，并实现了在不同 UGC 数据集上的最先进性能，具有非常好的实践应用前景。