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Proceedings Paper

Information theoretic methods for image processing algorithm optimization
Author(s): Sergey F. Prokushkin; Erez Galil
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Paper Abstract

Modern image processing pipelines (e.g., those used in digital cameras) are full of advanced, highly adaptive filters that often have a large number of tunable parameters (sometimes > 100). This makes the calibration procedure for these filters very complex, and the optimal results barely achievable in the manual calibration; thus an automated approach is a must. We will discuss an information theory based metric for evaluation of algorithm adaptive characteristics (“adaptivity criterion”) using noise reduction algorithms as an example. The method allows finding an “orthogonal decomposition” of the filter parameter space into the “filter adaptivity” and “filter strength” directions. This metric can be used as a cost function in automatic filter optimization. Since it is a measure of a physical “information restoration” rather than perceived image quality, it helps to reduce the set of the filter parameters to a smaller subset that is easier for a human operator to tune and achieve a better subjective image quality. With appropriate adjustments, the criterion can be used for assessment of the whole imaging system (sensor plus post-processing).

Paper Details

Date Published: 8 February 2015
PDF: 10 pages
Proc. SPIE 9396, Image Quality and System Performance XII, 939604 (8 February 2015); doi: 10.1117/12.2083293
Show Author Affiliations
Sergey F. Prokushkin, Roman Systems Engineering (United States)
Erez Galil, Univ. of California, Santa Cruz (United States)


Published in SPIE Proceedings Vol. 9396:
Image Quality and System Performance XII
Mohamed-Chaker Larabi; Sophie Triantaphillidou, Editor(s)

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