Smartphone Camera Self-Calibration Based on Sensors Reading Consistency

In large-scale series production the time for evaluating the camera spectral sensitivity is strongly limited and measured in units of seconds because of production and economic constraints. To estimate variation of spectral sensitivity properties, manufacturers usually precisely measure only a few sensors (the golden set) and use these measurements to perform quick estimation of any other sensor in the released pack. The main drawback of this approach is that the worst color reproduction error cannot be controlled for a particular device: instability of device production process usually causes significantly different sensors, which may not be included in the golden set. In that case the camera will work with low accuracy during the lifetime. To overcome this problem, we consider a new approach to camera spectral sensitivity estimation during its operation. The main idea is based on consistency estimation of images and average scenes spectra. Users receive such a combination of data in practice, for instance modern phone devices have built-in integral spectrometers. Also, the proposed approach can be considered in the scope of classical problem statement of spectral sensitivity estimation with color charts. In the paper we investigated the accuracy of the method of spectral sensitivity estimation based on the basis calculation with singular value decomposition of the sensitivities from the golden set in combination with different types of regularization.