A Novel No-Reference HD Video Quality Metric Based on Perceptual Temporal Pooling
Jie Xiang, Hamid Reza Tohidypour, Yixiao Wang, Panos Nasiopoulos, Mahsa T. Pourazad · 2023
Impressive advancements in capturing, display, and broadcasting technologies significantly elevate image and video quality, and with that the need for designing new reference and no-reference image and video quality metrics. One of the latest and perceptually accurate video quality metrics is the Video Multi-Method Assessment Fusion (VMAF) method. However, VMAF considers the temporal nature of video using basic average temporal pooling, an approach that falls short from human perception. In this paper, we introduce a new no-reference video quality metric that uses deep learning to extract spatial features and a unique temporal pooling approach to accurately predict the visual quality score. To this end, first we created a video quality dataset that consists of high-resolution, 20s-long test video clips compressed at several different bitrates. These videos were labeled based on subjective evaluations and were used to determine the perceptual importance of frames in our temporal pooling scheme. Evaluations showed that our proposed approach achieved correlation of 90.55% with human perception and outperformed the state-of-the-art VMAF approach by 15.63% accuracy.