9 February 2006 Handling of annoying variations of performances in video algorithm optimization
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Abstract
Evaluation and optimization, with an ever increasing variety of material, are getting more and more time-consuming tasks in video algorithm development. An additional difficulty in moving video is that frame-by-frame perceived performance can significantly differ from real-time perceived performance. This paper proposes a way to handle this difficulty in a more systematic and objective way than with usual long tuning procedures. We take the example of interpolation algorithms where variations of sharpness or contrast look annoying in real-time whereas the frame-by-frame performance looks well acceptable. These variations are analyzed to get an objective measure for the real-time annoyance. We show that the reason for the problem is that most interpolation algorithms are optimized across intraframe criteria ignoring that the achievable intrinsic performance may vary from frame to frame. Our method is thus based on interframe optimization taking into account the measured annoyance. The optimization criteria are steered frame by frame depending on the achievable performance of the current interpolation and the achieved performance in previous frames. Our policy can be described as "better be good all time than very good from time to time." The advantage is that it is automatically controlled by the compromise wished in the given application.
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Marina M. Nicolas, Marina M. Nicolas, } "Handling of annoying variations of performances in video algorithm optimization", Proc. SPIE 6057, Human Vision and Electronic Imaging XI, 60570T (9 February 2006); doi: 10.1117/12.642123; https://doi.org/10.1117/12.642123
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