Subjective evaluation of de-interlacing techniques

Zhao, Meng; Haan, G. de

doi:10.1117/12.586962

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…A general evaluation of de-interlacing methods has been given in [23,42], using the objective metric MSE. In [112], a subjective evaluation using paired comparison [91] of the various de-interlacing techniques is given. It reveals that in the context of video de-interlacing, the objective and subjective scores are highly correlated in a certain range.…”

Section: Discussionmentioning

confidence: 99%

Least‐Mean‐Square Adaptive Filters

Haykin¹,

Widrow²

2003

297

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Section: Discussionmentioning

confidence: 99%

Least‐Mean‐Square Adaptive Filters

Haykin¹,

Widrow²

2003

297

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“…Several different perceptual artifacts are related to this technology, ranging from the inversion of the top-field and the bottom-field, to the de-interlacing algorithms used in current display technologies. A technical overview has been provided in [16] while de-interlacing techniques have been subjectively compared in [72].…”

Section: Deinterlacing Artifactsmentioning

confidence: 99%

Hybrid video quality prediction: reviewing video quality measurement for widening application scope

Barkowsky

Sedano

Brunnström

et al. 2014

Multimed Tools Appl

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A tremendous number of objective video quality measurement algorithms have been developed during the last two decades. Most of them either measure a very limited aspect of the perceived video quality or they measure broad ranges of quality with limited prediction accuracy. This paper lists several perceptual artifacts that may be computationally measured in an isolated algorithm and some of the modeling approaches that have been proposed to predict the resulting quality from those algorithms. These algorithms usually have a very limited application scope but have been verified carefully. The paper continues with a review of some standardized and well-known video quality measurement algorithms that are meant for a wide range of applications, thus have a larger scope. Their individual artifacts prediction accuracy is usually lower but some of them were validated to perform sufficiently well for standardization. Several difficulties and shortcomings in developing a general purpose model with high prediction performance are identified such as a common objective quality scale or the behavior of individual indicators when confronted with stimuli that are out of their prediction scope. The paper concludes with a systematic framework approach to tackle the development of a hybrid video quality measurement in a joint research collaboration.

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