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.
There is a great deal of interest in methods to assess the perceptual quality of a video sequence in a full reference
framework. Motion plays an important role in human perception of video and videos suffer from several artifacts
that have to deal with inaccuracies in the representation of motion in the test video compared to the reference.
However, existing algorithms to measure video quality focus primarily on capturing spatial artifacts in the video
signal, and are inadequate at modeling motion perception and capturing temporal artifacts in videos. We present
an objective, full reference video quality index known as the MOtion-based Video Integrity Evaluation (MOVIE)
index that integrates both spatial and temporal aspects of distortion assessment. MOVIE explicitly uses motion
information from the reference video and evaluates the quality of the test video along the motion trajectories
of the reference video. The performance of MOVIE is evaluated using the VQEG FR-TV Phase I dataset and
MOVIE is shown to be competitive with, and even out-perform, existing video quality assessment systems.
In this Keynote Address paper, we review early work on Image and Video Quality Assessment against the backdrop of
an interpretation of image perception as a visual communication problem. As a way of explaining our recent work on
Video Quality Assessment, we first describe our recent successful advances on QA algorithms for still images,
specifically, the Structural SIMilarity (SSIM) Index and the Visual Information Fidelity (VIF) Index. We then describe
our efforts towards extending these Image Quality Assessment frameworks to the much more complex problem of
Video Quality Assessment. We also discuss our current efforts towards the design and construction of a generic and
publicly-available Video Quality Assessment database.
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