Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison
Abstract
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…
Citation impact
- FWCI
- 45.52
- Percentile
- 100%
- References
- 82
Authors
4- SCShyamprasad ChikkerurCorresponding
Arizona State University
- VSVijay Sundaram
Arizona State University
- MRMartin Reisslein
Arizona State University
- LJLina J. Karam
Arizona State University
Topics & keywords
- Video quality
- Computer science
- Subjective video quality
- PEVQ
- Artificial intelligence
- Metric (unit)
- Scene statistics
- Human visual system model