When video is transmitted over a packet-switched network, the sequence reconstructed at the receiver side might suffer from impairments introduced by packet losses, which can only be partially healed by the action of error concealment techniques. In this context we propose NORM (NO-Reference video quality Monitoring), an algorithm to assess the quality degradation of H.264/AVC video affected by channel errors. NORM works at the receiver side where both the original and the uncorrupted video content is unavailable. We explicitly account for distortion introduced by spatial and temporal error concealment together with the effect of temporal motion-compensation. NORM provides an estimate of the mean square error distortion at the macroblock level, showing good linear correlation (correlation coefficient greater than 0.80) with the distortion computed in full-reference mode. In addition, the estimate at the macroblock level can be successfully exploited by forward quality monitoring systems that compute quality objective metrics to predict mean opinion score (MOS) values. As a proof of concept, we feed the output of NORM to a reduced-reference quality monitoring system that computes an estimate of the structural similarity metric (SSIM) score, which is known to be well correlated with perceptual quality.
No-reference video quality monitoring of H.264/AVC coded video
NACCARI, MATTEO;TAGLIASACCHI, MARCO;TUBARO, STEFANO
2009-01-01
Abstract
When video is transmitted over a packet-switched network, the sequence reconstructed at the receiver side might suffer from impairments introduced by packet losses, which can only be partially healed by the action of error concealment techniques. In this context we propose NORM (NO-Reference video quality Monitoring), an algorithm to assess the quality degradation of H.264/AVC video affected by channel errors. NORM works at the receiver side where both the original and the uncorrupted video content is unavailable. We explicitly account for distortion introduced by spatial and temporal error concealment together with the effect of temporal motion-compensation. NORM provides an estimate of the mean square error distortion at the macroblock level, showing good linear correlation (correlation coefficient greater than 0.80) with the distortion computed in full-reference mode. In addition, the estimate at the macroblock level can be successfully exploited by forward quality monitoring systems that compute quality objective metrics to predict mean opinion score (MOS) values. As a proof of concept, we feed the output of NORM to a reduced-reference quality monitoring system that computes an estimate of the structural similarity metric (SSIM) score, which is known to be well correlated with perceptual quality.File | Dimensione | Formato | |
---|---|---|---|
04907075.pdf
Accesso riservato
:
Altro materiale allegato
Dimensione
1.29 MB
Formato
Adobe PDF
|
1.29 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.