ARC: Angle-Retaining Chromaticity diagram for color constancy error analysis

Angle-Retaining Chromaticity and Color Space: Invariants and Properties

The angle-retaining color space (ARC) and the corresponding chromaticity diagram encode information following a cylindrical color model. Their main property is that angular distances in RGB are mapped into Euclidean distances in the ARC chromatic components, making the color space suitable for data representation in the domain of color constancy. In this paper, we present an in-depth analysis of various properties of ARC: we document the variations in the numerical precisions of two alternative formulations of the ARC-to-RGB transformation and characterize how various perturbations in RGB impact the ARC representation. This was done empirically for the ARC diagram in a direct comparison against other commonly used chromaticity diagrams, and analytically for the ARC space with respect to its three components. We conclude by describing the color space in terms of perceptual uniformity, suggesting the need for new perceptual color metrics.

On the evaluation of temporal and spatial stability of color constancy algorithms

Computational color constancy algorithms are commonly evaluated only through angular error analysis on annotated datasets of static images. The widespread use of videos in consumer devices motivated us to define a richer methodology for color constancy evaluation. To this extent, temporal and spatial stability are defined here to determine the degree of sensitivity of color constancy algorithms to variations in the scene that do not depend on the illuminant source, such as moving subjects or a moving camera. Our evaluation methodology is applied to compare several color constancy algorithms on stable sequences belonging to the Gray Ball and Burst Color Constancy video datasets. The stable sequences, identified using a general-purpose procedure, are made available for public download to encourage future research. Our investigation proves the importance of evaluating color constancy algorithms according to multiple metrics, instead of angular error alone. For example, the popular fully convolutional color constancy with confidence-weighted pooling algorithm is consistently the best performing solution for error evaluation, but it is often surpassed in terms of stability by the traditional gray edge algorithm, and by the more recent sensor-independent illumination estimation algorithm.

Deep Dichromatic Guided Learning for Illuminant Estimation

A new dichromatic illuminant estimation method using a deep neural network is proposed. Previous methods based on the dichromatic reflection model commonly suffer from inaccurate separation of specularity, thus being limited in their use in a real-world. Recent deep neural network-based methods have shown a significant improvement in the estimation of the illuminant color. However, why they succeed or fail is not explainable easily, because most of them estimate the illuminant color at the network output directly. To tackle these problems, the proposed architecture is designed to learn dichromatic planes and their confidences using a deep neural network with novel losses function. The illuminant color is estimated by a weighted least mean square of these planes. The proposed dichromatic guided learning not only achieves compelling results among state-of-the-art color constancy methods in standard real-world benchmark evaluations, but also provides a map to include color and regional contributions for illuminant estimation, which allow for an in-depth analysis of success and failure cases of illuminant estimation.