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Video Quality Assessment (VQA) aims to simulate the process of perceivingvideo quality by the human visual system (HVS). The judgments made by HVS arealways influenced by human subjective feelings. However, most of the currentVQA research focuses on capturing various distortions in the spatial andtemporal domains of videos, while ignoring the impact of human feelings. Inthis paper, we propose CLiF-VQA, which considers both features related to humanfeelings and spatial features of videos. In order to effectively extractfeatures related to human feelings from videos, we explore the consistencybetween CLIP and human feelings in video perception for the first time.Specifically, we design multiple objective and subjective descriptions closelyrelated to human feelings as prompts. Further we propose a novel CLIP-basedsemantic feature extractor (SFE) which extracts features related to humanfeelings by sliding over multiple regions of the video frame. In addition, wefurther capture the low-level-aware features of the video through a spatialfeature extraction module. The two different features are then aggregatedthereby obtaining the quality score of the video. Extensive experiments showthat the proposed CLiF-VQA exhibits excellent performance on several VQAdatasets.

CLiF-VQA: Enhancing Video Quality Assessment by Incorporating High-Level Semantic Information related to Human Feelings