Video quality assessment (VQA) is a challenging problem due to the numerous
factors that can affect the perceptual quality of a video, \eg, content
attractiveness, distortion type, motion pattern, and level. However, annotating
the Mean opinion score (MOS) for videos is expensive and time-consuming, which
limits the scale of VQA datasets, and poses a significant obstacle for deep
learning-based methods. In this paper, we propose a VQA method named PTM-VQA,
which leverages PreTrained Models to transfer knowledge from models pretrained
on various pre-tasks, enabling benefits for VQA from different aspects.
Specifically, we extract features of videos from different pretrained models
with frozen weights and integrate them to generate representation. Since these
models possess various fields of knowledge and are often trained with labels
irrelevant to quality, we propose an Intra-Consistency and Inter-Divisibility
(ICID) loss to impose constraints on features extracted by multiple pretrained
models. The intra-consistency constraint ensures that features extracted by
different pretrained models are in the same unified quality-aware latent space,
while the inter-divisibility introduces pseudo clusters based on the annotation
of samples and tries to separate features of samples from different clusters.
Furthermore, with a constantly growing number of pretrained models, it is
crucial to determine which models to use and how to use them. To address this
problem, we propose an efficient scheme to select suitable candidates. Models
with better clustering performance on VQA datasets are chosen to be our
candidates. Extensive experiments demonstrate the effectiveness of the proposed
method.

本文介绍了一种名为 PTM-VQA 的视频质量评估方法，利用预训练模型从不同方面为 VQA 带来益处，并提出了 ICID 损失和有效的候选模型选择方案，实验证明了该方法的有效性。