ADS-VQA: Adaptive sampling model for video quality assessment

No-reference video quality assessment (NR-VQA) for user-generated content (UGC) plays a crucial role in ensuring the quality of video services. Although some works have achieved impressive results, their performance-complexity trade-off is still sub-optimal. On the one hand, overly complex network structures and additional inputs require more computing resources. On the other hand, the simple sampling methods have tended to overlook the temporal characteristics of the videos, resulting in the degradation of local textures and potential distortion of the thematic content, consequently leading to the performance decline of the VQA technologies. Therefore, in this paper, we propose an enhanced NR-VQA model, known as the Adaptive Sampling Strategy for Video Quality Assessment (ADS-VQA). Temporally, we conduct non-uniform sampling on videos utilizing features from the lateral geniculate nucleus (LGN) to capture the temporal characteristics of videos. Spatially, a dual-branch structure is designed to supplement spatial features across different levels. The one branch samples patches at their raw resolution, effectively preserving the local texture detail. The other branch performs a downsampling process guided by saliency cues, attaining global semantic features with a diminished computational expense. Experimental results demonstrate that the proposed approach achieves high performance at a lower computational cost than most state-of-the-art VQA models on four popular VQA databases.