When characterising a digital camera spectrally or colourimetrically, the camera response to a generally diffusely reflecting colour chart is often employed. The recorded responses to the light incident from each colour patch are typically not linearly related to the power of the irradiance on the chart, and the irradiance varies with position on the chart. This necessitates a linearisation of the responses. We present a new single image colour chart-based estimation method of responses, that are linearly related to camera response values known as ground truth. The method estimates the spatial geometry of the irradiance incident on the chart attenuated by lens vignetting and compensates individually for volumetric and per colour channel non-linearities, including compensation for physical scene and camera properties in a pipeline of successive signal transformations between the estimated linear and the given recorded responses. The estimation is controlled by introducing a novel Additivity Principle of linear responses, which is derived from the spectral reflectances of the coloured surfaces on the colour chart, observing that linear relations of the spectral reflectances are equal to the relations of the corresponding linear responses. Crucially, the additivity principle is not subject to metamerism. The method is fundamentally solely reliant on a one-shot set of one triplet of response values sampled from each patch of a colour chart with known spectral reflectances, where rendition level, gray scale, illuminant, camera sensor curves, irradiance geometry, vignetting, moderate specular reflection, colour space, colour correction, gamut correction and noise level are unknown.

Color correction relates device-dependent sensor responses (RGB) to device independent color values ($XYZ$). Here, we present a new approach to Hue-plane preserving color correction (HPPCC) using weighted constrained$3 \times 3$matrices. Hue-plane preservation was introduced in[1]in conjunction with an HPPCC method. That method maps using a finite number of local white point preserving$3 \times 3$matrices, each of which operates in a Hue-angle delimited subregion of device space defined by the white and two adjacent chromatic training set colors. However, that formulation does not leave room for optimization or continuity beyond$C^{0}$in the transitions between the subregions. To remedy that, our new method uses Hue-angle specific weighted matrixing: given a device RGB from which a device Hue-angle is derived, a corresponding transformation matrix is found as the normalized weighted sum of all precalculated constrained white point and training color preserving matrices. Each weight is calculated as a power function of the minimum difference between the device and the training color Hue-angle. The weighting function provides local influence to the matrices that are in close Hue-angle proximity to the device color. The power of the function is optimized for global accuracy. We call this HPPCC by weighted constrained matrixing.1Experiments performed using different input spectra show that our method consistently improves on both stability and accuracy compared with the state-of-the-art methods.1Patent Pending: PCT/2015/000054