Video Quality Assessment (VQA) aims to simulate the process of perceiving
video quality by the human visual system (HVS). The judgments made by HVS are
always influenced by human subjective feelings. However, most of the current
VQA research focuses on capturing various distortions in the spatial and
temporal domains of videos, while ignoring the impact of human feelings. In
this paper, we propose CLiF-VQA, which considers both features related to human
feelings and spatial features of videos. In order to effectively extract
features related to human feelings from videos, we explore the consistency
between CLIP and human feelings in video perception for the first time.
Specifically, we design multiple objective and subjective descriptions closely
related to human feelings as prompts. Further we propose a novel CLIP-based
semantic feature extractor (SFE) which extracts features related to human
feelings by sliding over multiple regions of the video frame. In addition, we
further capture the low-level-aware features of the video through a spatial
feature extraction module. The two different features are then aggregated
thereby obtaining the quality score of the video. Extensive experiments show
that the proposed CLiF-VQA exhibits excellent performance on several VQA
datasets.

本论文介绍了一种新的视频质量评估方法 CLiF-VQA，该方法考虑了与人类感受相关的特征和视频的空间特征，通过利用 CLIP 与人类感受之间的一致性，设计了多个客观和主观描述，提取与人类感受相关的特征。通过一个空间特征提取模块，还捕捉了视频的低级特征，并将这两种不同特征聚合以获得视频的质量评分。广泛的实验证明，所提出的 CLiF-VQA 在多个视频质量评估数据集上表现出色。