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User-generated content (UGC) live videos are often bothered by variousdistortions during capture procedures and thus exhibit diverse visualqualities. Such source videos are further compressed and transcoded by mediaserver providers before being distributed to end-users. Because of theflourishing of UGC live videos, effective video quality assessment (VQA) toolsare needed to monitor and perceptually optimize live streaming videos in thedistributing process. In this paper, we address \textbf{UGC Live VQA} problemsby constructing a first-of-a-kind subjective UGC Live VQA database anddeveloping an effective evaluation tool. Concretely, 418 source UGC videos arecollected in real live streaming scenarios and 3,762 compressed ones atdifferent bit rates are generated for the subsequent subjective VQAexperiments. Based on the built database, we develop a\underline{M}ulti-\underline{D}imensional \underline{VQA} (\textbf{MD-VQA})evaluator to measure the visual quality of UGC live videos from semantic,distortion, and motion aspects respectively. Extensive experimental resultsshow that MD-VQA achieves state-of-the-art performance on both our UGC Live VQAdatabase and existing compressed UGC VQA databases.

MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos