A Paradigm for color gamut mapping of pictorial images

In this thesis, a paradigm was generated for color gamut mapping of pictorial images. This involved the development and testing of: 1.) a hue-corrected version of the CIELAB color space, 2.) an image-dependent sigmoidal-lightness-rescaling process, 3.) an image-gamut- based chromatic-compression process, and 4.) a gamut-expansion process. This gamut-mapping paradigm was tested against some gamut-mapping strategies published in the literature. Reproductions generated by gamut mapping in a hue-corrected CIELAB color space more accurately preserved the perceived hue of the original scenes compared to reproductions generated using the CIELAB color space. The results of three gamut-mapping experiments showed that the contrast-preserving nature of the sigmoidal-lightness-remapping strategy generated gamut-mapped reproductions that were better matches to the originals than reproductions generated using linear-lightness-compression functions. In addition, chromatic-scaling functions that compressed colors at a higher rate near the gamut surface and less near the achromatic axis produced better matches to the originals than algorithms that performed linear chroma compression throughout color space. A constrained gamut-expansion process, similar to the inverse of the best gamut-compression process found in this experiment, produced reproductions preferred over an expansion process utilizing unconstrained linear expansion

Color gamut mapping in a hue-linearized CIELAB color space

Color gamut mapping plays a crucial role in color management. Depending on the application, it is sometimes desirable to perform color gamut mapping by shifting the lightness and compressing the chroma of an out-of-gamut color while preserving the perceived hue of the color. The term perceived hue is used to distinguish between the visual sensation of hue and metric hue angle (e.g., CIELAB hue angle (hab) ). If a gamut-mapping task constrains CIELAB metric hue angle in the blue region of CIELAB, a perceived-hue shift will result. Due to these nonlinearities, two hue-linearized versions of the CIELAB color space were generated, one from the Hung and Berns visual data (1995) and one from the Ebner and Fairchild data set (1998). Both data sets consist of visually mapped hue data to planes of constant visual hue. These modified versions of the CIELAB color space were psychophysically tested for their hue-linearity characteristics against the CIELAB color space. The results of these experiments show that, in the blue region of CIELAB, the hue-corrected color spaces are more visually uniform and perform better than CIELAB in gamut mapping situations with respect to perceived hue. However, the CIELAB color space performed as good as or better than either hue-corrected spaces outside of the blue region

From Pixels to Physics: Probabilistic Color De-Rendering

Consumer digital cameras use tone-mapping to produce compact, narrow-gamut images that are nonetheless visually pleasing. In doing so, they discard or distort substantial radiometric signal that could otherwise be used for computer vision. Existing methods attempt to undo these effects through deterministic maps that de-render the reported narrow-gamut colors back to their original wide-gamut sensor measurements. Deterministic approaches are unreliable, however, because the reverse narrow-to-wide mapping is one-to-many and has inherent uncertainty. Our solution is to use probabilistic maps, providing uncertainty estimates useful to many applications. We use a non-parametric Bayesian regression technique - local Gaussian process regression - to learn for each pixel's narrow-gamut color a probability distribution over the scene colors that could have created it. Using a variety of consumer cameras we show that these distributions, once learned from training data, are effective in simple probabilistic adaptations of two popular applications: multi-exposure imaging and photometric stereo. Our results on these applications are better than those of corresponding deterministic approaches, especially for saturated and out-of-gamut colors.Engineering and Applied Science

Implementing an ICC printer profile visualization software

Device color gamut plays a crucial role in ICC-based color management systems. Accurately visualizing a device\u27s gamut boundary is important in the analysis of color conversion and gamut mapping. ICC profiles contain all the information which can be used to better understand the capabilities of the device. This thesis project has implemented a printer profile visualization software. The project uses A2B 1 tag in a printer profile as gamut data source, then renders gamut of device the profile represents in CIELAB space with a convex hull algorithm. Gamut can be viewed interactively from any view points. The software also gets the gamut data set using CMM with different intent to do color conversion from a specified printer profile to a generic lab profile (short for A2B conversion) or from a generic CIELAB profile to a specified printer pro file and back to the generic CIELAB profile (short for B2A2B). Gamut can be rendered as points, wire frame or solid surface. Two-dimension a*b* and L*C* gamut slice analytic tools were also developed. The 2D gamut slice algorithm is based on dividing gamut into small sections according to lightness and hue angle. The point with maximum chroma on each section can be used to present a*b* gamut slice on a constant lightness plane or L*C* gamut slice on a constant hue angle plane. Gamut models from two or more device profiles can be viewed in the same window. Through the comparison, we can better understand the device reproduction capacities and proofing problems. This thesis also explained printer profile in details, and examined what gamut data source was the best for gamut visualization. At the same time, some gamut boundary descriptor algorithms were discussed. Convex hull algorithm and device space to CIELAB space mapping algorithm were chosen to render 3D gamut in this thesis project. Finally, an experiment was developed to validate the gamut data generated from the software. The experiment used the same method with profile visualization software to get gamut data set source from Photoshop 6.0. The results of the experiment were showed that the data set derived from visualization software was consistent with those from Photoshop 6.0

Cubical Gamut Mapping Colour Constancy

A new color constancy algorithm called Cubical Gamut Mapping (CGM) is introduced. CGM is computationally very simple, yet performs better than many currently known algorithms in terms of median illumination estimation error. Moreover, it can be tuned to minimize the maximum error. Being able to reduce the maximum error, possibly at the expense of increased median error, is an advantage over many published color constancy algorithms, which may perform quite well in terms of median illumination-estimation error, but have very poor worst-case performance. CGM is based on principles similar to existing gamut mapping algorithms; however, it represents the gamut of image chromaticities as a simple cube characterized by the image’s maximum and minimum rgb chromaticities rather than their more complicated convex hull. It also uses the maximal RGBs as an additional source of information about the illuminant. The estimate of the scene illuminant is obtained by linearly mapping the chromaticity of the maximum RGB, minimum rgb and maximum rgb values. The algorithm is trained off-line on a set of synthetically generated images. Linear programming techniques for optimizing the mapping both in terms of the sum of errors and in terms of the maximum error are used. CGM uses a very simple image pre-processing stage that does not require image segmentation. For each pixel in the image, the pixels in the Nby- N surrounding block are averaged. The pixels for which at least one of the neighbouring pixels in the N-by-N surrounding block differs from the average by more than a given threshold are removed. This pre-processing not only improves CGM, but also improves the performance of other published algorithms such as max RGB and Grey World

Attribute-preserving gamut mapping of measured BRDFs

Reproducing the appearance of real-world materials using current printing technology is problematic. The reduced number of inks available define the printer's limited gamut, creating distortions in the printed appearance that are hard to control. Gamut mapping refers to the process of bringing an out-of-gamut material appearance into the printer's gamut, while minimizing such distortions as much as possible. We present a novel two-step gamut mapping algorithm that allows users to specify which perceptual attribute of the original material they want to preserve (such as brightness, or roughness). In the first step, we work in the low-dimensional intuitive appearance space recently proposed by Serrano et al. [SGM*16], and adjust achromatic reflectance via an objective function that strives to preserve certain attributes. From such intermediate representation, we then perform an image-based optimization including color information, to bring the BRDF into gamut. We show, both objectively and through a user study, how our method yields superior results compared to the state of the art, with the additional advantage that the user can specify which visual attributes need to be preserved. Moreover, we show how this approach can also be used for attribute-preserving material editing

Gamut Mapping for Pictorial Images

A psychophysical evaluation was performed to test the quality of several color gamut mapping algorithms. The task was to determine which mapping strategy produced the best matches to the original image. Observer preference was not considered. The algorithms consisted of both device dependent and image-dependent mappings. Three types of lightness scaling functions (linear compression, chroma weighted linear compression, and image dependent sigmoidal compression) and four types of chromatic mapping functions were tested (linear compression, knee-point compression, sigmoidlike compression, and clipping). The source and destination devices considered were a monitor and a plain-paper inkjet printer respectively. The results showed that, for all of the images tested, the algorithms that used image-dependent sigmoidal lightness remapping functions produced superior matches to those that utilized linear lightness scaling. In addition, the results support using chromatic compression functions that were closely related to chromatic clipping functions

Spectral Gamut Mapping and Gamut Concavity

A spectral gamut-mapping algorithm is introduced that works well for printers with a large number of inks. It finds the best mapping onto the convex hull of the printer spectral gamut while preserving color defined in CIE XYZ as much as possible. The technique employs a non-negative least-square fit. Since the gamut-mapping algorithm depends on the common assumption that the gamut is convex, an experimental study of the degree of gamut concavity is conducted. It finds that there is a significant amount of concavity, and that that the degree does not appear to change much as the number of inks is increased. Finally, the performance of the gamut-mapping algorithm and gamut coverage in spectral space is compared for 3-, 4-, 5- and 6-ink printers using both synthetic ink models and real ink data

A Color Gamut Description Algorithm for Liquid Crystal Displays in CIELAB Space

Because the accuracy of gamut boundary description is significant for gamut mapping process, a gamut boundary calculating method for LCD monitors is proposed in this paper. Within most of the previous gamut boundary calculation algorithms, the gamut boundary is calculated in CIELAB space directly, and part of inside-gamut points are mistaken for the boundary points. While, in the new proposed algorithm, the points on the surface of RGB cube are selected as the boundary points, and then converted and described in CIELAB color space. Thus, in our algorithm, the true gamut boundary points are found and a more accurate gamut boundary is described. In experiment, a Toshiba LCD monitor's 3D CIELAB gamut for evaluation is firstly described which has regular-shaped outer surface, and then two 2D gamut boundaries (CIE- * * boundary and CIE- * * boundary) are calculated which are often used in gamut mapping process. When our algorithm is compared with several famous gamut calculating algorithms, the gamut volumes are very close, which indicates that our algorithm's accuracy is precise and acceptable