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Conference object . 2018
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https://doi.org/10.1109/cbmi.2...
Article . 2018 . Peer-reviewed
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Refining and Evaluating a Video Sharpness Metric

Authors: Stefanie Wechtitsch; Werner Bailer; Lucas Paletta;

Refining and Evaluating a Video Sharpness Metric

Abstract

Automatic quality assessment is an essential step in the professional audiovisual media production process. In this paper we propose a novel sharpness metric taking the specific properties of video into account, and having a higher robustness against variations in image content, interlacing artifacts and noise. Furthermore, a comprehensive user study is presented, where we obtain subjective scores to validate the sharpness metric. We ask 28 viewers for both absolute and relative judgments of sharpness in two separate experiment settings, supported with an eye tracker to obtain the locations used for judgements. Experimental results show that the objective sharpness metric under test is well correlated with human perception. Both results, the absolute and relative subjective ratings confirm the good correlation of the proposed metric with human perception. The analysis of the eye tracking data highlights differences between experts and consumers.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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