A No-reference Perceptual Quality Metric for Videos Distorted by Spatially Correlated Noise

Assessing the perceptual quality of videos is critical for monitoring and optimizing video processing pipelines. In this paper, we focus on predicting the perceptual quality of videos distorted by noise. Existing video quality metrics are tuned for“white”, i.e., spatially uncorrelated noise. However, white noise is very rare in real videos. Based on our analysis of the noise correlation patterns in a broad and comprehensive video set, we build a video database that simulates the commonly encountered noise characteristics. Using the database, we develop a perceptual quality assessment algorithm that explicitly incorporates the noise correlations. Experimental results show that, for videos with spatially correlated noises, the proposed algorithm presents high accuracy in predicting perceptual qualities. CCS Concepts •Human-centered computing → User models; Laboratory experiments; Empirical studies in HCI;