关键词: 短视频, 视频质量评价, 结构特征, CLIP模型, 人类视觉系统

Abstract: To address the difficulty in effectively assessing the quality of short videos with rich content and complex structure, a method based on the Contrastive Language-Image Pre-Training (CLIP) model is proposed. Firstly, according to the special form of short videos, an efficient structural feature extraction module is designed to capture its textual and layout characteristics. Next, to enhance the representation of global features, a multi-feature extractor is developed to capture quality features across three dimensions: the spatiotemporal quality, the structural quality, and the perceptual quality, ensuring comprehensive coverage of semantic information and distortion characteristics. Finally, a text input template is constructed to guide the quality feature fusion process using the CLIP features extracted from video frames. Experimental results on four benchmark datasets demonstrate that the proposed method achieves superior accuracy and stability. Specifically, on the KVQ short video dataset, the Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank order Correlation Coefficient (SRCC) reach 0.922 and 0.919, respectively. On the TaoLive livestream dataset, the two metrics show an average improvement of 1.1% compared to the second-best method. In terms of generalization, the cross-dataset performance achieves an average improvement of 1.6%, which is suitable for a wide range of application scenarios.

Key words: short video, video quality assessment, structural feature, Contrastive Language-Image Pre-Training model, Human visual system