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

Video Quality Assessment (VQA) aims to simulate the process of perceiving video quality by the Human Visual System (HVS). Although subjective studies have shown that the judgments of HVS are strongly influenced by human feelings, it remains unclear how video content relates to human feelings. The recent rapid development of Vision-Language pre-trained models (VLM) has established a solid link between language and vision. And human feelings can be accurately described by language, which means that VLM can extract information related to human feelings from visual content with linguistic prompts. In this paper, we propose CLiF-VQA, which innovatively utilizes the visual linguistic capabilities of VLM to introduce human feelings features based on traditional spatio-temporal features to more accurately simulate the perceptual process of HVS. In order to efficiently extract features related to human feelings from videos, we pioneer the exploration of the consistency between Contrastive Language-Image Pre-training (CLIP) and human feelings in video perception. In addition, we design effective prompts, i.e., a variety of objective and subjective descriptions closely related to human feelings, as prompts. Extensive experiments show that the proposed CLiF-VQA exhibits excellent performance on several VQA datasets. The results show that introducing human feelings features on top of spatio-temporal features is an effective way to obtain better performance.