KVQ: Kwai Video Quality Assessment for Short-form Videos
Abstract
Short-form UGC video platforms like Kwai and TikTok have been an emerging and irreplaceable mainstream media form thriving on user-friendly engagement and kaleidoscope creation etc. However the advancing content generation modes e.g. special effects and sophisticated processing workflows e.g. de-artifacts have introduced significant challenges to recent UGC video quality assessment: (i) the ambiguous contents hinder the identification of quality-determined regions. (ii) the diverse and complicated hybrid distortions are hard to distinguish. To tackle the above challenges and assist in the development of short-form videos we establish the first large-scale Kwai short Video database for Quality assessment termed KVQ which comprises 600 user-uploaded short videos and 3600 processed videos through the diverse practical processing workflows including pre-processing transcoding and enhancement. Among them the absolute quality score of each video and partial ranking score among indistinguish samples are provided by a team of professional researchers specializing in image processing. Based on this database we propose the first short-form video quality evaluator i.e. KSVQE which enables the quality evaluator to identify the quality-determined semantics with the content understanding of large vision language models (i.e. CLIP) and distinguish the distortions with the distortion under- standing module. Experimental results have shown the effectiveness of KSVQE on our KVQ database and popular VQA databases. The project can be found at https: //lixinustc.github.io/projects/KVQ/.