A No-reference Perceptual Quality Metric for Videos Distorted by Spatially Correlated noise
Venue
ACM Multimedia 2016, Amsterdam, The Netherlands (to appear)
Publication Year
2016
Authors
Chao Chen, Mohammad Izadi, Anil Kokaram
BibTeX
Abstract
Assessing the perceptual quality of video is critical for monitoring and optimizing
video processing pipelines. In this paper, we focus on predicting the perceptual
quality for videos distorted by noise. Existing video quality metrics are generally
focus on ``white", i.e., spatially un-correlated noise. However, white noise is
very rare in realistic videos. Based on our analysis of the noise correlation
patterns on a large and comprehensive video set, we build a video database that
simulates the commonly encountered noise patterns. Using the database, we develop a
perceptual quality metric that explicitly incorporates the noise pattern in quality
prediction. Experimental results show that the proposed algorithm presents very
high correlation with the perceptual quality of noisy videos.
