A Multi-annotated and Multi-modal Dataset for Wide-angle Video Quality Assessment

TL;DR

This paper introduces the first multi-annotated, multi-modal dataset for wide-angle video quality assessment, highlighting the limitations of existing methods and emphasizing the need for specialized datasets in this domain.

Contribution

The creation of the MWV dataset fills a critical gap, enabling better evaluation and development of video quality assessment methods for wide-angle videos.

Findings

Existing methods perform poorly on the MWV dataset.

Wide-angle video quality assessment requires specialized datasets.

Standard methods have significant limitations on wide-angle videos.

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.