For purposes of defining a feasible approach to spectral color management, previous research proposed an interim connection space (ICS). ICS is relatively low in dimension and would be situated between a high-dimensional spectral profile connection space and output units. The current research simulated printed spectra after using a multidimensional ICS-based lookup tables (LUTs) based on LabPQR, an ICS described in earlier work. LabPQR has three colorimetric dimensions (CIELAB) and additional dimensions to describe a metameric black (PQR). The spectral reproduction accuracies for printing on a six-color ink jet printer were compared based on several versions of the ICS-based LUTs. Variations were evaluated with respect to quality trade-offs between size of the LUT and spectral reproduction accuracies, as well as the number of dimensions necessary for spectral color management. A five-dimensional 17×17×17×5×3 LUT performed well with three dimensions for CIELAB and two dimensions for the PQR metameric black space. This LUT resulted in average CIEDE2000 of 0.51 and average spectral root mean square error of 4.22% for a simulated spectral reproduction of the GretagMacbeth ColorChecker.

The penetration of toner into porous substrates is studied using cross sections and topographical maps of the toner layer. Toner penetrates readily into the pores of the substrate and the penetration depth is larger than previously stated. As the toner enters the fixing stage, its temperature increases, which leads to a decrease in its viscosity. As a result, the toner penetrates and flows more easily into the pores of the substrate. The nip pressure pushes the toner layer against the substrate, thus promoting the penetration significantly. The toner layer also adapts to the irregularities of the paper surface. Toner does not seem to penetrate into 0.1 μm pores. This indicates that toner cannot penetrate into the pores of paper coatings. The surface energy of the substrate seems to increase toner penetration slightly by enhancing wetting, spreading, and penetration potential. Other substrate properties were not observed to significantly affect toner penetration. However, these effects of the substrate properties are almost totally overrun by the pressure in the nip.

A generalized clustering algorithm utilizing the geometrical shapes of clusters for segmentation of colored brain immunohistological images is presented. To simplify the computation, the dimension of vectors composed from the pixel RGB components is reduced from three to two by applying a de-correlation mapping with the orthogonal bases of the eigenvectors of the auto-covariance matrix. Since the brain immunohistochemical images have stretched clusters that appear long and narrow in geometrical shape, we use centroids of straight lines instead of single points to approximate the clusters. An iterative algorithm is developed to optimize the linear centroids by minimizing the approximation mean-squared error. The partitioning of the two-dimensional vector domain into three portions classifies each image pixel into one of the three classes: The microglial cell cytoplasm, the combined hematoxylin stained cell nuclei and the neuropil, and the pale background. Regions of the combined hematoxylin stained cell nuclei and the neuropil are to be separated based on the differences in their regional shapes. The segmentation results of real immunohistochemical images of brain microglia are provided and discussed.

Color look-up tables (CLUTs) that provide transformations between various color spaces are commonly embedded in printer firmware where they are stored in relatively expensive flash memory. As the number of look-up tables in color devices increases in size, the space requirements of storing these CLUTs also increase. In order to conserve memory and thereby reduce cost, it is desirable to compress CLUTs prior to storage and restore tables as required. We consider methods for improving the performance of existing lossless compression methods for this application through computationally simple preprocessing. The preprocessing combines predictive coding and data reordering to better exploit the redundancy in CLUT data. Two predictive coding methods are considered: (a) hierarchical differential encoding methods, which generalizes differential coding to multiple dimensions, and (b) cellular interpolative predictive coding, which refines a CLUT in a coarse to fine order using interpolative prediction. Space filling curves that preserve continuity in the multidimensional CLUT structure are utilized for reordering the residuals obtained from hierarchical differential encoding. For the cellular interpolative prediction, we reorder the data in the coarse to fine order utilized for prediction. Results indicate that the proposed preprocessing methods offer significant performance improvements in comparison with direct compression. The best performance is obtained using the cellular interpolative predictive coding and corresponding reordering with the LZMA algorithm. This method provides a compression ratio of 3.19 over our representative CLUT data set, and an improvement of 31.33% over direct LZMA compression, the latter being the best performing direct method.

Spectral encoding/decoding methods using unique base functions and physically meaningful values were explored. Three new methods such as, TrW6 consisting of six unique trigonometric functions, Lab2 consisting of two CIELAB functions, and LabRGB consisting of CIELAB and RGB, were derived and compared against the traditional eigenvectors method. It was found that TrW6 and LabRGB showed almost the same accuracy as the traditional eigenvector method. By using LabRGB, color characteristics can be estimated by only looking at its encoding values and we do not have to exchange base functions beforehand for exchanging a different population of object colors. LabRGB can be applied not only to spectral imaging but also to traditional trichromatic imaging world, so its use can extend beyond spectral uses.

This paper proposes a reversible algorithm to convert color images to gray images while keeping chroma and spatial resolution. The original method of Braun and de Queiroz [Ref. 5] had theoretical problems such as deterioration in both chroma and spatial resolution. The authors describe a solution for these problems by devising a color embedding technique. A semi-invariant algorithm for chroma and spatial resolution can be realized by replacing a subband of the luminance component with the quantized high-pass luminance and chrominance signals. Experimental results show that the proposed algorithm can recover vivid color images from the textured gray images of the original color images, while keeping spatial resolution. Furthermore, the ability of color recovery from coded gray images is evaluated from the point of view of image coding using JPEG and JPEG2000.

A large-scale psychophysical experiment was performed to establish the important image appearance attributes controlling the perceived quality of images presented on a large display under dark surround conditions. Six image appearance attributes were chosen: Colorfulness, contrast, naturalness, visual information, sharpness, and image quality. A nine-point qualitative category scale was used to rate these six attributes for eight test images, each of which had 22 derivative images which varied in lightness, chroma, and sharpness. The influences of the three image manipulations on the six attributes, and the psychophysical relations between image quality and its constituent attributes were investigated. Multiple regression and factor analysis were conducted to derive an empirical image quality model. It was found that there were high correlations among the five attributes forming image quality: Between sharpness and contrast, and between naturalness and visual information. Furthermore, colorfulness, contrast, and naturalness were key attributes to have a significant impact on image quality.

The purpose of this work was to quantify the surround effect on the color image appearance of images presented on a large display. The appearance attributes colorfulness, contrast, and naturalness together with image quality were estimated under four surround conditions: Dark, dim, and bright surrounds (all excluding veiling glare), and a typical office environment (including veiling glare). The most interesting visual result was that the three illuminated surrounds caused dark images to have a reduction in all of the image appearances studied, compared with those under dark surrounds. Enhanced contrast, naturalness, and quality were perceived in dark surround conditions, whereas the most colorful appearance was seen with bright surrounds. The most critical attribute affecting image quality in all surrounds was naturalness. Empirical models were developed which took into account the significant visual phenomena revealed in this study to predict the effect of surround on the appearance of color images.

This article proposes a new algorithm for illuminant estimation based on the concept of chromagenic color constancy, where two pictures are taken from each scene: A normal one and one where a colored filter is placed in front of the camera. The basic formulation of the chromagenic algorithm has inherent weaknesses, namely, a need for perfectly registered images and occasional large errors in illuminant estimation. Our first contribution is to analyze the algorithm performance with respect to the reflectances present in a scene and demonstrate that fairly bright and de-saturated reflectances (e.g., achromatic and pastel colors) provide significantly better chromagenic illuminant estimation. We thus propose the bright chromagenic algorithm and show that it not only remedies the large error problem but also allows us to relax the image registration constraint. Experiments performed on a variety of synthetic and real data show that the newly designed bright chromagenic algorithm significantly outperforms current illuminant estimation methods, including those having a substantially higher complexity.

Illuminant estimation in natural scenes includes the problem of estimating a spatial distribution of light sources by omnidirectional observations. So far most omnidirectional measuring systems used a mirrored ball as an optical tool. This kind of system, however, has several essential problems, such as low and nonuniform spatial resolution and a dead region due to imaging the camera itself, as reflected by the mirror. The present article realizes multiband spectral imaging with high spatial resolution for omnidirectional estimation of scene illuminant in a simple measuring system. To overcome the mirrored ball problems, we propose a multiband omnidirectional imaging system using a high-resolution trichromatic digital camera, a fisheye lens, and two commercial color filters. The spatial resolution of omnidirectional imaging systems is analyzed based on an optics model in detail. We describe a practical spectral imaging system. Use of the RGB camera with each of the two color filters allows two different sets of trichromatic spectral sensitivity functions via spectral multiplication, resulting in six spectral bands, after image capture with each color filter. An algorithm is developed based on statistical estimation theory for estimating illuminant spectra from noisy observations of the sensor outputs. The feasibility of the proposed method is examined from the viewpoints of spatial resolution and omnidirectional illuminant estimation.