  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
Kaleidoscope 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 indistinguishable 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 understanding module.
Experimental results have shown the effectiveness of KSVQE on our KVQ database
and popular VQA databases.
