TY - JOUR
T1 - Objective video quality assessment methods
T2 - A classification, review, and performance comparison
AU - Chikkerur, Shyamprasad
AU - Sundaram, Vijay
AU - Reisslein, Martin
AU - Karam, Lina
N1 - Funding Information:
Manuscript received August 09, 2010; revised December 12, 2010; accepted December 20, 2010 Date of publication February 10, 2011; date of current version May 25, 2011. This work was supported in part by the National Science Foundation under Grant CRI-0750927.
PY - 2011/6
Y1 - 2011/6
N2 - With the increasing demand for video-based applications, the reliable prediction of video quality has increased in importance. Numerous video quality assessment methods and metrics have been proposed over the past years with varying computational complexity and accuracy. In this paper, we introduce a classification scheme for full-reference and reduced-reference media-layer objective video quality assessment methods. Our classification scheme first classifies a method according to whether natural visual characteristics or perceptual (human visual system) characteristics are considered. We further subclassify natural visual characteristics methods into methods based on natural visual statistics or natural visual features. We subclassify perceptual characteristics methods into frequency- or pixel-domain methods. According to our classification scheme, we comprehensively review and compare the media-layer objective video quality models for both standard resolution and high definition video. We find that the natural visual statistics based MultiScale-Structural SIMilarity index (MS-SSIM), the natural visual feature based Video Quality Metric (VQM), and the perceptual spatio-temporal frequency-domain based MOtion-based Video Integrity Evaluation (MOVIE) index give the best performance for the LIVE Video Quality Database.
AB - With the increasing demand for video-based applications, the reliable prediction of video quality has increased in importance. Numerous video quality assessment methods and metrics have been proposed over the past years with varying computational complexity and accuracy. In this paper, we introduce a classification scheme for full-reference and reduced-reference media-layer objective video quality assessment methods. Our classification scheme first classifies a method according to whether natural visual characteristics or perceptual (human visual system) characteristics are considered. We further subclassify natural visual characteristics methods into methods based on natural visual statistics or natural visual features. We subclassify perceptual characteristics methods into frequency- or pixel-domain methods. According to our classification scheme, we comprehensively review and compare the media-layer objective video quality models for both standard resolution and high definition video. We find that the natural visual statistics based MultiScale-Structural SIMilarity index (MS-SSIM), the natural visual feature based Video Quality Metric (VQM), and the perceptual spatio-temporal frequency-domain based MOtion-based Video Integrity Evaluation (MOVIE) index give the best performance for the LIVE Video Quality Database.
KW - Full-reference metric
KW - objective video quality
KW - perceptual video quality
KW - reduced-reference metric
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U2 - 10.1109/TBC.2011.2104671
DO - 10.1109/TBC.2011.2104671
M3 - Article
AN - SCOPUS:79957780748
SN - 0018-9316
VL - 57
SP - 165
EP - 182
JO - IEEE Transactions on Broadcasting
JF - IEEE Transactions on Broadcasting
IS - 2 PART 1
M1 - 5710601
ER -