首个宽视角视频质量评估数据集，解决真实场景下画质评价难题
A Multi-annotated and Multi-modal Dataset for Wide-angle Video Quality Assessment
- 构建多标注、多模态的宽视角视频质量数据集
- 现有方法在该数据集上表现受限，泛化能力差
- 适合视频质量评估与宽视角应用研究者使用
宽视角视频因其广阔的视场和大范围场景捕捉能力，广泛应用于体育与户外记录。然而，其易产生形变、曝光等问题，导致画质下降，影响观看体验，限制了在竞技体育等领域的应用。目前针对宽视角视频质量评估的研究极少，主要因缺乏专用数据集。为此，本文构建了首个多标注、多模态的宽视角视频质量评估（MWV）数据集，并通过跨数据集与内数据集测试，评估当前主流视频质量评估方法的表现。实验结果表明，现有方法在该数据集上存在显著局限性，适用性不足。
原文摘要 · Abstract (English)
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.
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