Concise Radiometric Calibration Using The Power of Ranking

Han Gong, Graham Finlayson and Maryam Darrodi

Abstract

Compared with raw images, the more common JPEG images are less useful for machine vision algorithms and professional photographers because JPEG-sRGB does not preserve a linear relation between pixel values and the light measured from the scene. A camera is said to be radiometrically calibrated if there is a computational model which can predict how the raw linear sensor image is mapped to the corresponding rendered image (e.g. JPEGs) and vice versa. This paper begins with the observation that the rank order of pixel values are mostly preserved post colour correction. We show that this observation is the key to solving for the whole camera pipeline (colour correction, tone and gamut mapping). Our rank-based calibration method is simpler than the prior art and so is parametrised by fewer variables which, concomitantly, can be solved for using less calibration data. Another advantage is that we can derive the camera pipeline from a single pair of raw-JPEG images.

Session

Posters

Files

Paper (PDF)
Supplementary (PDF)

DOI

10.5244/C.31.27
https://dx.doi.org/10.5244/C.31.27

Citation

Bibtex

            @inproceedings{BMVC2017_27,
                title={Concise Radiometric Calibration Using The Power of Ranking},
                author={Han Gong, Graham Finlayson and Maryam Darrodi},
                year={2017},
                month={September},
                pages={27.1-27.11},
                articleno={27},
                numpages={11},
                booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
                publisher={BMVA Press},
                editor={Tae-Kyun Kim, Stefanos Zafeiriou, Gabriel Brostow and Krystian Mikolajczyk},
                doi={10.5244/C.31.27},
                isbn={1-901725-60-X},
                url={https://dx.doi.org/10.5244/C.31.27}
            }