Colour constancy using von Kries transformations: colour constancy "goes to the Lab"

Colour constancy algorithms aim at correcting colour towards a correct perception within scenes. To achieve this goal they estimate a white point (the illuminant's colour), and correct the scene for its in uence. In contrast, colour management performs on input images colour transformations according to a pre-established input pro le (ICC pro le) for the given con- stellation of input device (camera) and conditions (illumination situation). The latter case presents a much more analytic approach (it is not based on an estimation), and is based on solid colour science and current industry best practises, but it is rather in exible towards cases with altered conditions or capturing devices. The idea as outlined in this paper is to take up the idea of working on visually linearised and device independent CIE colour spaces as used in colour management, and to try to apply them in the eld of colour constancy. For this purpose two of the most well known colour constancy algorithms White Patch Retinex and Grey World Assumption have been ported to also work on colours in the CIE LAB colour space. Barnard's popular benchmarking set of imagery was corrected with the original imple- mentations as a reference and the modi ed algorithms. The results appeared to be promising, but they also revealed strengths and weaknesses

Print engine color management using customer image content

The production of quality color prints requires that color accuracy and reproducibility be maintained to within very tight tolerances when transferred to different media. Variations in the printing process commonly produce color shifts that result in poor color reproduction. The primary function of a color management system is maintaining color quality and consistency. Currently these systems are tuned in the factory by printing a large set of test color patches, measuring them, and making necessary adjustments. This time-consuming procedure should be repeated as needed once the printer leaves the factory. In this work, a color management system that compensates for print color shifts in real-time using feedback from an in-line full-width sensor is proposed. Instead of printing test patches, this novel attempt at color management utilizes the output pixels already rendered in production pages, for a continuous printer characterization. The printed pages are scanned in-line and the results are utilized to update the process by which colorimetric image content is translated into engine specific color separations (e.g. CIELAB-\u3eCMYK). The proposed system provides a means to perform automatic printer characterization, by simply printing a set of images that cover the gamut of the printer. Moreover, all of the color conversion features currently utilized in production systems (such as Gray Component Replacement, Gamut Mapping, and Color Smoothing) can be achieved with the proposed system

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

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

Choosing Colors for Geometric Graphs via Color Space Embeddings

Graph drawing research traditionally focuses on producing geometric embeddings of graphs satisfying various aesthetic constraints. After the geometric embedding is specified, there is an additional step that is often overlooked or ignored: assigning display colors to the graph's vertices. We study the additional aesthetic criterion of assigning distinct colors to vertices of a geometric graph so that the colors assigned to adjacent vertices are as different from one another as possible. We formulate this as a problem involving perceptual metrics in color space and we develop algorithms for solving this problem by embedding the graph in color space. We also present an application of this work to a distributed load-balancing visualization problem.Comment: 12 pages, 4 figures. To appear at 14th Int. Symp. Graph Drawing, 200

Gamut extension algorithm development and evaluation for the mapping of standard image content to wide-gamut displays

Wide-gamut display technology has provided an excellent opportunity to produce visually pleasing images, more so than in the past. However, through several studies, including Laird and Heynderick, 2008, it was shown that linearly mapping the standard sRGB content to the gamut boundary of a given wide-gamut display may not result in optimal results. Therefore, several algorithms were developed and evaluated for observer preference, including both linear and sigmoidal expansion algorithms, in an effort to define a single, versatile gamut expansion algorithm (GEA) that can be applied to current display technology and produce the most preferable images for observers. The outcome provided preference results from two displays, both of which resulted in large scene dependencies. However, the sigmoidal GEAs (SGEA) were competitive with the linear GEAs (LGEA), and in many cases, resulted in more pleasing reproductions. The SGEAs provide an excellent baseline, in which, with minor improvements, could be key to producing more impressive images on a wide-gamut display

Evaluation of the color image and video processing chain and visual quality management for consumer systems

With the advent of novel digital display technologies, color processing is increasingly becoming a key aspect in consumer video applications. Today’s state-of-the-art displays require sophisticated color and image reproduction techniques in order to achieve larger screen size, higher luminance and higher resolution than ever before. However, from color science perspective, there are clearly opportunities for improvement in the color reproduction capabilities of various emerging and conventional display technologies. This research seeks to identify potential areas for improvement in color processing in a video processing chain. As part of this research, various processes involved in a typical video processing chain in consumer video applications were reviewed. Several published color and contrast enhancement algorithms were evaluated, and a novel algorithm was developed to enhance color and contrast in images and videos in an effective and coordinated manner. Further, a psychophysical technique was developed and implemented for performing visual evaluation of color image and consumer video quality. Based on the performance analysis and visual experiments involving various algorithms, guidelines were proposed for the development of an effective color and contrast enhancement method for images and video applications. It is hoped that the knowledge gained from this research will help build a better understanding of color processing and color quality management methods in consumer video