The introduction of digital intermediate workflow in movie production has made visualization of the final image
on the film set increasingly important. Images that have been color corrected on the set can also serve as a basis
for color grading in the laboratory. In this paper we suggest and evaluate an approach that has been used to
simulate the appearance of different film stocks. The GretagMacbeth Digital ColorChecker was captured using
both a Canon EOS 20D camera as well as an analog camera. The film was scanned using an Arri film
scanner. The images of the color chart were then used to perform a colorimetric characterization of these devices
using models based on polynomial regression. By using the reverse model of the digital camera and the forward
model of the analog film chain, the output of the film scanner was simulated. We also constructed a direct
transformation using regression on the RGB values of the two devices. A different color chart was then used as
a test set to evaluate the accuracy of the transformations, where the indirect model was found to provide the
required performance for our purpose without compromising the flexibility of having an independent profile for
Gamut mapping algorithms are currently being developed to take advantage of the spatial information in an
image to improve the utilization of the destination gamut. These algorithms try to preserve the spatial information
between neighboring pixels in the image, such as edges and gradients, without sacrificing global contrast.
Experiments have shown that such algorithms can result in significantly improved reproduction of some images
compared with non-spatial methods. However, due to the spatial processing of images, they introduce unwanted
artifacts when used on certain types of images. In this paper we perform basic image analysis to predict whether
a spatial algorithm is likely to perform better or worse than a good, non-spatial algorithm. Our approach starts
by detecting the relative amount of areas in the image that are made up of uniformly colored pixels, as well
as the amount of areas that contain details in out-of-gamut areas. A weighted difference is computed from
these numbers, and we show that the result has a high correlation with the observed performance of the spatial
algorithm in a previously conducted psychophysical experiment.
A method is proposed for performing spectral gamut mapping, whereby spectral images can be altered to fit within an approximation of the spectral gamut of an output device. Principal component analysis (PCA) is performed on the spectral data, in order to reduce the dimensionality of the space in which the method is applied. The convex hull of the spectral device measurements in this space is computed, and the intersection between the gamut surface and a line from the center of the gamut towards the position of a given spectral
reflectance curve is found. By moving the spectra that are outside the spectral gamut towards the center until the gamut is encountered, a spectral gamut mapping algorithm is defined. The spectral gamut is visualized by approximating the intersection of the gamut and a 2-dimensional plane. The resulting outline is shown along with the center of the gamut and the position of a spectral reflectance curve. The spectral gamut mapping algorithm is applied to spectral data from the Macbeth Color Checker and test images, and initial results show that the amount of clipping increases with the number of dimensions used.