A Dataset and Method for Assessing the Quality of Display Devices

This paper proposes a novel objective quality assessment method for display devices, aiming to comprehensively evaluate their overall quality, with a particular focus on the user’s subjective experience. By constructing a dedicated video dataset and integrating a no-reference video quality assessment (NR VQA) model, we directly learn spatiotemporal features related to quality from video frames and introduce a color attention mechanism to enhance the model’s sensitivity to color distortions. The model consists of feature extraction, quality regression, and quality pooling modules, capable of extracting quality-aware features from spatial, spatiotemporal, and color domains, and determining the final quality score using a temporal averaging pooling strategy. Experimental results demonstrate that the proposed model performs excellently in evaluating the overall quality of display devices, showing high practical applicability. The main contributions of this paper include constructing a video dataset, proposing a new model, and introducing a color attention mechanism, which effectively enhances the comprehensive evaluation of the subjective quality of display devices, filling the gap in display device quality assessment.