A Multiscale Objective Function for Camera Color Correction

Color correction (CC) plays a pivotal role in camera imaging. Existing approaches usually conduct CC tuning by minimizing ∆E (e.g. ∆E2000), a standard metric proposed by CIE for representing color differences in LAB space. However, we observe that not all the colors with identical ∆E error to the target color have with same perceptual preference. Consequently, optimizing CC by minimizing ∆E solely does not always produce satisfactory color-rendition accuracy. To deal with the problem, in this paper, we propose a new score function, namely Ψ, for a more accurate discrimination of different color-rendition mappings. This is achieved by a multi-scale objective incorporating not only ∆E, but also ∆H and ∆C, which respectively indicate color differences from hue and chroma perspectives. We describe the details of Ψ and show how to adjust its parameters for different preferences. We verify the usefulness of Ψ in experiments by embedding it in various CC tuning algorithms. The empirical results show that Ψ consistently leads to better color-rendition accuracy not only in training but also in validation sets. Finally, we deploy our new objective for tuning a real-world commercial digital camera and show that it delivers improved performance.