This work proposes a study of the Piecewise Linear assuming Variation in Chromaticity (PLVC) display color characterization model. This model has not been widely used as the improved accuracy compared with the more common PLCC (Piecewise Linear assuming Chromaticity Constancy) model is not significant for CRT (Cathode Ray Tube) display technology, and it requires more computing power than this model. With today's computers, computational complexity is less of a problem, and today's display technologies show a different colorimetric behavior than CRTs. The main contribution of this work is to generalize the PLVC model to multiprimary displays and to provide extensive experimental results and analysis for today's display technologies. We confirm and extend the results found in the literature and compare this model with classical PLCC and Gain-Offset-Gamma-Offset models. We show that using this model is highly beneficial for Liquid Crystal Displays, reducing the average error about a third for the two tested LCD projectors compared with a black corrected PLCC model, from 3.93 and 1.78 to respectively 1.41 and 0.54 DE Ã ab units.

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We have defined an inverse model for colorimetric characterization of additive displays. It is based on an optimized three-dimensional tetrahedral structure. In order to minimize the number of measurements, the structure is defined using a forward characterization model. Defining a regular grid in the device-dependent destination color space leads to heterogeneous interpolation errors in the device-independent source color space. The parameters of the function used to define the grid are optimized using a globalized Nelder-Mead simplex downhill algorithm. Several cost functions are tested on several devices. We have performed experiments with a forward model which assumes variation in chromaticities (PLVC), based on one-dimensional interpolations for each primary ramp along X, Y and Z (3 × 3 × 1 − D). Results on 4 devices (2 LCD and a DLP projection devices, one LCD monitor) are shown and discussed.

We describe some applications of linear and nonlinear projection methods in order to reduce the number of spectral bands in Landsat multispectral images. The nonlinear method is curvilinear component analysis ͑CCA͒, and we propose an adapted optimization of it for image processing, based on the use of principal-component analysis ͑PCA, a linear method͒. The principle of CCA consists in reproducing the topology of the original space projection points in a reduced subspace, keeping the maximum of information. Our conclusions are: CCA is an improvement for dimension reduction of multispectral images; CCA is really a nonlinear extension of PCA; CCA optimization through PCA ͑called CCAinitPCA͒ allows a reduction of the computation burden but provides a result identical to that of CCA.