Concise Radiometric Calibration Using The Power of Ranking

TL;DR

This paper introduces a simple, rank-based radiometric calibration method that effectively models the camera pipeline using minimal data, achieving state-of-the-art results especially for JPEG to raw image conversion.

Contribution

The proposed method leverages pixel rank preservation to calibrate camera models with fewer parameters and less data, simplifying the calibration process.

Findings

Outperforms existing methods in JPEG to raw calibration

Requires only a single pair of raw-JPEG images for calibration

Achieves state-of-the-art accuracy in radiometric calibration

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. Experiments demonstrate that our method delivers state-of-the-art results (especially for the most interesting case of JPEG to raw).