A hybrid video quality metric for analyzing quality degradation due to frame drop
This work addresses video quality assessment for human viewers, but it appears incremental as it builds on existing metrics.
The paper tackles the problem of assessing video quality degradation, particularly due to frame drop, by presenting a full reference hybrid metric that analyzes spatial, temporal, or spatio-temporal distortions, with simulated results showing it efficiently analyzes degradation and aligns closely with the human visual system.
In last decade, ever growing internet technologies provided platform to share the multimedia data among different communities. As the ultimate users are human subjects who are concerned about quality of visual information, it is often desired to have good resumed perceptual quality of videos, thus arises the need of quality assessment. This paper presents a full reference hybrid video quality metric which is capable to analyse the video quality for spatially or temporally (frame drop) or spatio-temporally distorted video sequences. Simulated results show that the metric efficiently analyses the quality degradation and more closer to the developed human visual system