In this article the authors show that image quality measures can be successfully used to develop image-individualized gamut mapping algorithms. First the authors compare different image quality measures for the gamut mapping problem and then validate them using psychovisual data from four recent gamut mapping studies. The scoring function used to validate the quality measures is the hit rate, i.e., the percentage of correct choice predictions on data from psychovisual tests. Some of the image quality measures predict the observer's preferences as good as scaling methods such as Thurstones method, which is used to evaluate the psychovisual tests. This is remarkable because the scaling methods are based on the experimental data, whereas the quality measures are independent of these data. The best performing image quality measure is used to automatically select the optimal gamut mapping algorithm for an individual image.

The authors propose a novel method to map a multispectral image into the device independent color space CIE-XYZ. This method provides a way to visualize multispectral images by predicting colorvalues from spectral values while maintaining interpretability and is tested on a light emitting diode based multispectral system with a total of 11 channels in the visible area. To obtain interpretable models, the method estimates the projection coefficients with regard to their neighbors as well as the target. This results in relatively smooth coefficient curves which are correlated with the CIE-XYZ color matching functions. The target of the regression is a well known color chart, and the models are validated using leave one out cross validation in order to maintain best possible generalization ability. The authors compare the method with a direct linear regression and see that the interpretability improves significantly but comes at the cost of slightly worse predictability.

Digital imaging has seen an unprecedented growth in the past five years. The variety of imaging systems available to users to create and view visual data is enormous. Color management has become an important aspect of modern imaging and display systems. Color profiles have been the de facto tool for achieving faithful visual reproduction for a long time. In this article, the authors discuss issues associated with profile-based color management systems. The authors describe an alternative approach motivated by the problem of visually matching two known display devices. The authors use a model-based method to achieve this goal and propose realizing the method with simple table look-up operations. The authors devise a framework for designing look-up tables (LUTs) which are optimal in terms of color reproduction on the displays based on resource constraints. The LUT-based color management system is shown to be more accurate and memory-efficient than a comparable International Color Consortium profile-based system.

In this article, the authors investigate and study the color spatial uniformity of projectors. A common assumption in previous works is to consider that only the luminance is varying along the spatial dimensions. The authors show that the chromaticity plays a significant role in the spatial color shift and should not be disregarded depending on the application. The authors base their conclusions on the measurements obtained from three projectors. First, two methods are used to analyze the spatial properties of the projectors, a conventional approach, and a new one that considers three-dimensional gamut differences. The results show that the color gamut difference between two spatial coordinates within the same display can be larger than the difference observed between two projectors. In a second part, the authors focus on the evaluation of assumptions commonly made in projector color characterization. The authors investigate if these assumptions are still valid along the spatial dimensions. Features studied include normalized response curve, chromaticity constancy of primaries, and channel independence. Some features seem to vary noticeably spatially, such as the normalized response curve. Some others appear to be quite invariant, such as the channel independence.

Redeye is a well-known problem in portrait photography. This effect is caused by the light entering the subject's eye through the pupil and reflection from the retina back to the imaging sensor. Many of the existing redeye correction methods sometimes fail to detect the actual redeye artifacts and incorrectly change other red colored areas. The proposed algorithm describes a fully automatic redeye correction system with multilevel eye confirmation stages. The algorithm first identifies the skin and locates the redeye region using color information. The detected region is then confirmed as redeye by its redness variation, glint, eye-lips triangle, and comparison with surrounding regions. The algorithm removes the falsely extracted components by verifying with rules derived from the spatial and geometrical relationships of facial components. Finally the defect is corrected by desaturating the red pixels while preserving the natural glint presence.

In this article the authors analyze the influence of the number of samples in a training set on the accuracy of color and spectral measurements made using a colorimetric and multispectral imaging system. The authors develop a method for establishing the minimum and/or sufficient number of color samples in the training set above which the system's performance is independent of the number of samples. The authors also consider the dependence of the system's performance on the training set itself. Two setups of a charge coupled device camera-based imaging system are used for this purpose: a colorimetric configuration with three acquisition channels and a multispectral configuration with seven acquisition channels. On the basis of the criterion established in this article, the results show that the system's performance in terms of the accuracy of color measurement and spectral reconstruction seems to be independent of the training set used when over 110 samples are used for the colorimetric configuration and over 120 samples for the multispectral configuration. This result is true for both the number of samples in the training set and the training set itself. The method that the authors developed can be generalized and implemented in the industry for any application in which an imaging capture device is used for color and spectral measurements.

Despite the evident success of conventional one-dimensional (1D) bar codes and their role in automatic product identification, logistics, supply, and retail, these 1D technologies can encode only a limited amount of data and are susceptible to damage and obscuration. On the other hand, two-dimensional (2D) codes which began appearing in the 1980s are becoming more and more popular because of their ability to encode a large amount of data in a small area. They can also be read even if they are partially damaged or erased. Because of their advantages in comparison to linear bar codes and their increasingly frequent use the authors analyzed two 2D codes (DATA MATRIX code and QR code). The purpose of our research was to determine the lowest raster tone value for each printing color and each printing technique which would enable the 2D code reading with two different readers. For testing the readability of codes, lightfastness, accelerated aging, and water resistance were used. Codes were created and printed in four process inks (cyan, magenta, yellow and black), each of them in the following raster tone values: 15%, 20%, 25%, 30%, 50%, and 100%. Samples were printed using three different digital printing technologies (ink jet, laser, and magnetography). After printing, the samples were illuminated by Xe light, exposed to accelerated aging (at 80°C and 65% relative humidity) for six days, and immersed in water to determine their water resistance. All printed samples, before and after illumination and accelerated aging, were read with laser and charge coupled device based readers, and the lowest raster tone value of the code for each printing technique and each printing ink was determined.

Region saliency has not been fully considered in most previous image quality assessment models. In this article, the contribution of any region to the global quality measure of an image is weighted with variable weights computed as a function of its saliency. In salient regions, the differences between distorted and original images are emphasized as if the authors are observing the difference image with a magnifying glass. Here a mixed saliency map model based on Itti's model and face detection is proposed. Both low-level features including intensity, color, orientation, and high-level features such as face are used in the mixed model. Differences in salient regions are then given more importance and thus contribute more to the image quality score. The experiments done on the 1700 distorted images of the TID2008 database show that the performance of the image quality assessment on full subsets is enhanced.

In the transfer process of electrophotography, electrostatic forces caused by the transfer electric field act on a sheet in the nip region. As a result, the sheet sticks to the photoconductor drum and sheet transport fails. The authors have developed a numerical simulation method of sheet transport in the transfer process. The method calculates sheet transport by means of weak coupling between electric field and structural analyses and considers electrostatic forces acting on the sheet and displacement of components relative to each other. The authors used the method to calculate sheet transport in a system that consists of two rollers and the sheet between them. In this system, the sheet passes between two rollers which contact each other and to which a voltage is applied. The calculated charge density, discharges, and sheet displacement showed good agreement with experiments. Another analysis clarified the relationship between electric discharges in the nip region and the sheet sticking to the roller.