Ultra high definition video quality assessment (UHD VQA) is challenging due to the high spatio-temporal dimensions of UHD content. In this work, we propose a spatio-temporal framework based on feature fusion and selection to facilitate UHD VOA. First, we introduce a spatially adaptive fusion net (SAFNet) that integrates all patch-wise features into a compact frame-wise representation via local aggregation and global interaction. During the aggregation and the interaction, the contribution of each patch-wise feature is initially estimated via visual saliency-derived weights and then adaptively refined by contextual adjustments. Second, we design a temporally adaptive selection net (TASNet) to reduces temporal redundancy. It retains a small number of temporal positions through thresholding a dynamically estimated importance vector, which captures both representativeness and inter-frame variation. Finally, extensive experiments conducted on 4 datasets with various 4K videos demonstrate the effectiveness of the proposed method in comparison with some traditional and state-of-the-art VQA methods.

To enhance the viewer experience of standard dynamic range (SDR) video content on high dynamic range (HDR) displays, inverse tone mapping (ITM) is employed. Objective visual quality assessment (VQA) models are needed for effective evaluation of ITM algorithms. However, there is a lack of specialized VQA models for assessing the visual quality of inversely tone-mapped HDR videos (ITM-HDR-Videos). This paper addresses both an algorithmic and a dataset gap by introducing a novel SDR referenced HDR (SD-R-HD) VQA model tailored for ITM-HDR-Videos, along with the first public dataset specifically constructed for this purpose. The innovations of the SD-R-HD VQA model include 1) utilizing available SDR video as a reference signal, 2) extracting features that characterize standard ITM operations such as global mapping and local compensation, and 3) directly modeling interframe inconsistencies introduced by ITM operations. The newly created ITM-HDR-VQA dataset comprises 200 ITM-HDR-Videos annotated with mean opinion scores, gathered over 320 man-hours of psychovisual experiments. Experimental results demonstrate that the SD-R-HD VQA model significantly outperforms existing state-of-the-art VQA models.