A Multi-annotated and Multi-modal Dataset for Wide-angle Video Quality Assessment
Bo Hu, Wei Wang, Chunyi Li, Lihuo He, Leida Li, Xinbo Gao
TL;DR
This work tackles the gap in wide-angle video quality assessment by introducing MWV, a first multi-annotated and multi-modal dataset tailored to wide-angle distortions. It documents a comprehensive data collection, a structured subjective study with four distortion attributes, and rigorous data processing that yields reliable MOS values, plus textual descriptions to capture perceptual cues. Through inter- and intra-dataset experiments, the paper demonstrates that state-of-the-art VQA methods trained on standard datasets fail to generalize to wide-angle content, even when trained on MWV itself, highlighting the need for specialized models that address deformation and severe shake. The MWV dataset thus enables targeted development of wide-angle VQA methods with potential impact on sports, adventure, and other wide-view applications where perceptual quality is critical.
Abstract
Wide-angle video is favored for its wide viewing angle and ability to capture a large area of scenery, making it an ideal choice for sports and adventure recording. However, wide-angle video is prone to deformation, exposure and other distortions, resulting in poor video quality and affecting the perception and experience, which may seriously hinder its application in fields such as competitive sports. Up to now, few explorations focus on the quality assessment issue of wide-angle video. This deficiency primarily stems from the absence of a specialized dataset for wide-angle videos. To bridge this gap, we construct the first Multi-annotated and multi-modal Wide-angle Video quality assessment (MWV) dataset. Then, the performances of state-of-the-art video quality methods on the MWV dataset are investigated by inter-dataset testing and intra-dataset testing. Experimental results show that these methods impose significant limitations on their applicability.