Accurate device-independent colorimetric measurements using smartphones.

Abstract

Smartphones provide an ideal platform for colorimetric measurements due to their low cost, portability and image quality. As with any imaging-based colorimetry system, ambient light and device variations introduce error which must be dealt with. We propose a novel processing method consisting of a one-time calibration stage to account for inter-phone variations, and an innovative use of ambient light subtraction with image pairs to account for variation in ambient light. Data collection is kept very simple, making it particularly useful for use in the field, since nothing additional is required in the images. Ambient subtraction is first demonstrated for a range of colors and phones (Samsung S8 and LG Nexus 5X), and the Subtracted Signal to Noise Ratio (SSNR) is defined as a metric for assessing whether an image pair is appropriate at the time of image capture. The experimentally determined SSNR threshold below which to suggest retaking the images is 3.4. The classification accuracy for results using the proposed calibration pipeline is then compared to the simplest image metadata-based alternative and is found to be greatly superior. Finally, a custom colorcard is shown to improve the accuracy of device-independent results for known smaller ranges of colors over a standard colorcard, making this a possible application-specific modification to the overall processing pipeline.