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Perceptually based parameter adjustments for video processing operations
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. (Computer Graphics and Image Processing)
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. (Computer Graphics and Image Processing)ORCID iD: 0000-0002-7765-1747
Bangor University, United Kingdom.
Bangor University, United Kingdom.
2014 (English)In: ACM SIGGRAPH Talks 2014, ACM Press, 2014Conference paper, Abstract (Refereed)
Abstract [en]

Extensive post processing plays a central role in modern video production pipelines. A problem in this context is that many filters and processing operators are very sensitive to parameter settings and that the filter responses in most cases are highly non-linear. Since there is no general solution for performing perceptual calibration of image and video operators automatically, it is often necessary to manually perform tweaking of multiple parameters. This is an iterative process which requires instant visual feedback of the result in both the spatial and temporal domains. Due to large filter kernels, computational complexity, high frame rate, and image resolution it is, however, often very time consuming to iteratively re-process and tweak long video sequences.We present a new method for rapidly finding the perceptual minima in high-dimensional parameter spaces of general video operators. The key idea of our algorithm is that the characteristics of an operator can be accurately described by interpolating between a small set of pre-computed parameter settings. By computing a perceptual linearization of the parameter space of a video operator, the user can explore this interpolated space to find the best set of parameters in a robust way. Since many operators are dependent on two or more parameters, we formulate this as a general optimization problem where we let the objective function be determined by the user’s image assessments. To demonstrate the usefulness of our approach we show a set of use cases (see the supplementary material) where our algorithm is applied to computationally expensive video operations.

Place, publisher, year, edition, pages
ACM Press, 2014.
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-106952OAI: diva2:720373
ACM SIGGRAPH 2014, 10-14 August, Vancouver, Canada
Available from: 2014-05-28 Created: 2014-05-28 Last updated: 2015-09-22Bibliographically approved

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