We propose a family of image quality assessment (IQA) models based on natural scene statistics (NSS), that can predict the subjective quality of a distorted image without reference to a corresponding distortionless image, and without any training results on human opinion scores of distorted images. These `completely blind' models compete well with standard non-blind image quality indices in terms of subjective predictive performance when tested on the large publicly available `LIVE' Image Quality database.
KEYWORDS: Video, Databases, Video compression, Visualization, Distortion, Data modeling, Visual process modeling, Performance modeling, Video processing, Telecommunications
Automatic methods to evaluate the perceptual quality of a digital video sequence have widespread applications
wherever the end-user is a human. Several objective video quality assessment (VQA) algorithms exist, whose
performance is typically evaluated using the results of a subjective study performed by the video quality experts
group (VQEG) in 2000. There is a great need for a free, publicly available subjective study of video quality that
embodies state-of-the-art in video processing technology and that is effective in challenging and benchmarking
objective VQA algorithms. In this paper, we present a study and a resulting database, known as the LIVE
Video Quality Database, where 150 distorted video sequences obtained from 10 different source video content
were subjectively evaluated by 38 human observers. Our study includes videos that have been compressed by
MPEG-2 and H.264, as well as videos obtained by simulated transmission of H.264 compressed streams through
error prone IP and wireless networks. The subjective evaluation was performed using a single stimulus paradigm
with hidden reference removal, where the observers were asked to provide their opinion of video quality on
a continuous scale. We also present the performance of several freely available objective, full reference (FR)
VQA algorithms on the LIVE Video Quality Database. The recent MOtion-based Video Integrity Evaluation
(MOVIE) index emerges as the leading objective VQA algorithm in our study, while the performance of the
Video Quality Metric (VQM) and the Multi-Scale Structural SIMilarity (MS-SSIM) index is noteworthy. The
LIVE Video Quality Database is freely available for download1 and we hope that our study provides researchers
with a valuable tool to benchmark and improve the performance of objective VQA algorithms.
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