Showing papers presented at "Color Imaging Conference in 2008"

Colouring the near infrared

Abstract: Current digital camera sensors are inherently sensitive to the near-infrared part of the spectrum To prevent the near-IR contamination of images, an IR blocking filter (hot mirror) is placed in front of the sensor In this work, we start by replacing the camera's hot mirror by a piece of clear glass, thus making the camera sensitive to both visible and near-IR light Using a pair of lens-mounted filters, we explore the differences in operating the camera to take visible and near-IR images of a given scene Our aim is to enhance the visible images using near-IR information To do so, we first discuss the physical causes of differences between visible and near-IR natural images, and remark that these causes are not correlated with a particular colour, but with atmospheric conditions and surface characteristics We then investigate image enhancement by considering the near-IR channel as either colour, luminance, or frequency counterpart to the visible image and conclude that using information from two different colour encodings, depending on the image content, produces vivid, contrasted images that are pleasing to the observers

Watermark encoding and detection using narrow band illumination

Abstract: A system is employed to reveal a watermark in a document. A watermark generator is utilized to select the placement and at least one colorant combination of an image and at least one colorant combination for a watermark on a document, where the at least one colorant combination of the image and the watermark form a metameric pair. A printing system receives data from the watermark generator and places the image and the watermark on the document. A decoder comprising a narrow band illumination element is selected or tuned to a wavelength corresponding to the colorant combinations utilized by the printing system to reveal the watermark placed thereon.

Multispectral high dynamic range imaging

Abstract: Capturing natural scenes with high dynamic range content using conventional RGB cameras generally results in saturated and underexposed and therefore compromising image areas. Furthermore the image lacks color accuracy due to a systematic color error of the RGB color filters. The problem of the limited dynamic range of the camera has been addressed by high dynamic range imaging1, 2 (HDRI): Several RGB images of different exposures are combined into one image with greater dynamic range. Color accuracy on the other hand can be greatly improved using multispectral cameras,3 which more accurately sample the electromagnetic spectrum. We present a promising combination of both technologies, a high dynamic range multispectral camera featuring a higher color accuracy, an improved signal to noise ratio and greater dynamic range compared to a similar low dynamic range camera.© (2008) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Alpha, contrast and the perception of visual metadata.

Abstract: Visual elements such as grids, labels, and contour lines act as “reference structures” or “visual metadata” that support the primary information being presented. Such structures need to be usefully visible, but not so obtrusive that they clutter the presentation. Our goal is to determine the physical, perceptual and cognitive characteristics of such structures, ideally in a way that enables their automatic computation. We present the result of a set of experiments to determine effective display ranges, described in terms of transparency (alpha), for thin rectangular grids over scatterplot data. These show that an effective range can be defined in terms of alpha. In an effort to create a display-independent set of metrics, we analyze these results in terms of luminance contrast, with mixed results. We conclude that the appearance of transparency is an important aspect of subtle visualization. Introduction Visual elements such as grids act as reference structures or visual metadata that support the primary information being presented. Such structures need to be usefully visible, but not so obtrusive that they clutter the presentation. Other static examples include labels and contour lines. Interactive techniques like smart cursors and object handles also create reference structures. Visual designers expertly manipulate properties such as color, line weight and transparency to create a balance between reference structures and the critical data. The broad goal of our research is to create engineering metrics and models that enable dynamic, algorithmically generated displays to be similarly effective. Our approach to this problem is not to characterize “ideal” or “best,” but instead to define boundary conditions, outside of which the result is clearly bad. We reason that the best solution will always be influenced by both context and taste. Boundary conditions, however, are more likely to have simple rules that can easily be incorporated by engineers and researchers. By eliminating, or at least reducing, the most objectionable cases, we can more easily raise the overall quality of computer-generated presentations. This paper will summarize the results from a first set of experiments to characterize the boundary conditions for rectangular grids. In these experiments, the subjects manipulated the transparency (alpha) of thin-line grids overlaid on scatterplots of different complexities rendered on backgrounds of different lightnesses (all grayscale imagery). The goal was to find a range of alpha values that create acceptably subtle grids. Our results show that a statistically acceptable range of alpha values can be established for our experimental conditions, which used the same calibrated display for all subjects. To create a display-independent model, we need to tie our results to perceptual metrics such as luminance contrast, which is used, for example, to provide metrics for text legibility. Our analysis shows that contrast alone is insufficient to explain our results, suggesting that the degree of transparency may be the more critical metric. This paper will first discuss reference structures from a design perspective, then summarize our experiments and their results. We then provide an analysis of our results in terms of contrast. We conclude with our directions for future work, focusing on image complexity and further explorations of transparency metrics. Subtle Design Designers create subtle reference structures by vary visual contrast, typically manipulating color, line weight and transparency [1]. Figure 1 shows a grid overlaid on a map. The lines that define the grid in Figure 1(b) appear lighter (actually, more transparent) and are thinner than those in Figure 1(a), resulting in a more subtle appearance. The overall goal of the designer is to achieve a well-balanced composition of visual layers, in which whatever constitutes the “figure” is well defined with respect to “ground”. Grids and other visual metadata live somewhere in the middle of these layers, where sometimes the grid needs to be more figure (visually accessible for search or reference) and sometimes more ground (relegated to the background and not intrusive.) Figure 1(a). A badly designed grid that obscures the underlying information. Figure 1 (b). This grid is more subtle, allowing the viewer to focus

A Probabilistic Model of the Categorical Association Between Colors.

Abstract: In this paper we describe a non-parametric probabilistic model that can be used to encode relationships in color naming datasets. This model can be used with datasets with any number of color terms and expressions, as well as terms from multiple languages. Because the model is based on probability theory, we can use classic statistics to compute features of interest to color scientists. In particular, we show that the uniqueness of a color name (color saliency) can be captured using the entropy of the probability distribution. We demonstrate this approach by applying this model to two different datasets: the multi-lingual World Color Survey (WCS), and a database collected via the web by Dolores Labs. We demonstrate how saliency clusters similarly named colors for both datasets, and compare our WCS results to those of Kay and his colleagues. We compare the two datasets to each other by converting them to a common colorspace (IPT). Introduction There has been growing interest in how to use color naming data to improve color models. Better color name databases[7, 10, 11, 12, 14, 2] and online naming studies[18, 8] have stimulated recent work. Color naming databases and associated models have been been useful in color transfer[5], gamut mapping[19, 20], and methods for specifying or selecting colors in an image[15, 16, 17]. In this paper, we examine the issue of how to represent and quantify the association between colors induced by names. Current methods that incorporate naming data represent the category associated with a color using either a single name[5, 6], a vector[19], or by a set of fuzzy logic memberships[1, 2, 17]. We present a probabilistic framework for working with colors. We define the categorical association of a color c as a conditional probability P(C|c) over colors C in the color space C . For a color c, the probability P(C|c) represents how likely other colors in the space C are assigned the same linguistic label as c. Our choice of using a probability over colors in our framework is motivated by the following criteria not met by current approaches. Our model satisfies three design goals. (1) Our approach can incorporate categorical effects from any number of color words, expressions involving multiple words, and different languages. (2) Our framework is based on a non-parametric model which can capture the differences in color name distributions such as “yellow” having a narrow focus and “green” having a wide distribution[21]. (3) Embedding our representation in a probabilistic framework enables us to apply a wide array of statistical and probabilistic tools to further analyze and study the effect of categories on colors. We implement our model on two datasets. We extract color naming data from six languages in the World Color Survey which contains naming information at 330 colors on the surface of the Munsell solid[7]. We also investigate online naming data collected by DoloresLabs which contains names given to 10,000 randomly sampled colors in the RGB cube[8]. Our framework can incorporate cross-linguistic data and combine contributions from color words with similar meanings. We introduce the concept of salient colors based on the statistical notion of entropy. Salient colors from our approach show good correspondence basic color terms identified by Berlin and Kay[3]. Our approach also reveals two regions that are consistently named in the sRGB cube not corresponding to typical basic color terms. We compare qualitatively the differences in salient name regions between the World Color Survey and the DoloresLabs datasets. Motivations and Related Work The goal of this paper is to present a computational framework for modeling color categories derived from experimental data. Our framework is motivated by three issues that are at best partially addressed in the current literature. 1. We would like a framework that can include all possible words for describing a color and not be limited to a predefined list of terms. 2. We would like a non-parametric model capable of capturing the details in categorical association but still be robust to noise in the naming dataset. 3. We would like a framework that can support a rich set of computational and mathematical operations, so that more in-depth studies of categorical effects can be built on the framework. In particular, our approach is grounded in probability theory. The first issue addresses how to account for the many potential expressions for describing a color. In 1969, Berlin and Kay defined color words as basic color terms if their meanings cannot be derived from other words, and proposed that there are a total of eleven basic color terms. Basic color terms were shown to be universal across languages. While some languages such as English contain all eleven terms, others may have developed only a subset of the words[3]. Subsequent studies confirmed that basic color terms are words with the highest consensus between speakers[4], but found twelve basic color terms in Russian contradicting the limit on the number of terms[22]. Kay and McDaniel hypothesized that as languages evolve, some individuals may consider additional words such as aqua/turquoise (green and blue), chartreuse/lime (yellow and green), and maroon/burgundy (red and black) as basic color terms[9]. Many existing methods assume eleven or a fixed number of color categories and cannot process the full set of responses from recent surveys such as the HP Labs Multilingual Naming Experiment[18] and the DoloresLabs Naming Dataset[8], which have hundreds of color words. Chang et al.’s category-preserving color transfer algorithm defines eleven convex regions in the color space corresponding to the basic color terms[5]. Motomura’s categorical color mapping algorithm maps foci of the eight chromatic basic color terms between the source and target gamuts[19]. Moroney’s system for translating colors to names operates on the n most frequently used color words. We want a framework where all words are included and contributions from words with similar cognitive concepts such as “maroon” and “burgundy” are combined based on their similarity. Secondly, color names exhibit different naming distributions. Colors such as “red” and “yellow” are known to have a narrow and well-defined center while colors such as “green” and “blue” are known to be composed of a broad range of hue.[21] We want our framework have the flexibility to capture the details in the distributions while being robust to noise in the data. Current approaches tend to model color categories as a volume in color space, using various parameterized models, or using non-parameterized approaches such as histograms. Partitioning the color space[12, 5] assume color names occupy discrete and non-overlapping regions in the color space. Motomura’s gamut-mapping algorithm assumes that each basic term has an ellipsoid-shaped distribution and models the distributions using an 81-parameter covariance matrix[19]. Benavente models the color naming space using a set of 6-parameter SigmoidGaussian distributions[1]. One advantage of parameterized models is that they are constructed from a small number of parameters which can be estimated accurately. In his adaptive lexical classification system, Moroney proposes an alternative implementation in which color names are represented as non-parametric histograms[16]. While histograms can capture any shape of distribution, Moroney reported noise in the data due to limited number of data points and suggests that smoothing operators or hedging be applied to post-process the histograms.1 Finally, we would like a framework capable of supporting a rich set of computational and mathematical tools. Instead of being merely a representation, the framework should allows us to perform further computation and analysis on how categories affect the way we associate colors. Treating the association between colors as a probability distribution positions our framework within the well-studied domain of probability theory. Methodology Colors and Color Words A naming dataset consists of a list of responses in the form of “color”-“color word” pairs that record the words used to describe a color. A “color” refers to the stimuli shown to a respondent and varies between datasets from Munsell color chips viewed under controlled lighting to rectangles of colors displayed on uncalibrated monitors. Unconstrained surveys allow respondents to use any expression whereas constrained surveys ask respondents to choose from a predefined list of words. An unconstrained color expression could include, e.g., “granny smith apple green”, “light robin’s egg pastel blue”, or “mix all the paint together”. In practice, most expressions recorded in unconstrained surveys consistent of a single word or a simple set of words such as “blue” or 1We should emphasize our application differs from Moroney’s in that his work is on modeling the distribution of color names while our work is on modeling the association between colors due to naming effects. “bluish green”. We will use the term “color words” from this point on even though it could refer to any possible expressions for describing a color. A naming dataset can be tabulated using a word count table where the list of all colors presented in the survey is displayed along the columns, and a list of all color words recorded is displayed along the rows. Each entry in the table indicates the number of times a corresponding color word is used to describe the corresponding color. Depending on the nature of the naming dataset, the density of word count table may vary. The World Color Survey (WCS)[7] is cross-linguistic and unconstrained, and collects naming data on a set of 330 colors. The word count table for the WCS consisting of 2300 rows by 330 columns with 20% non-zero entries. In comparison, the DoloresLabs color name dataset[8] while also unconstrained uses 10000 randomly-sampled colors. A total of 1966 expressi

Quaternion Color Curvature

Abstract: In this paper we propose a novel approach to measuring curvature in color or vector-valued images (up to 4-dimensions) based on quaternion singular value decomposition of a Hessian matrix. This approach generalizes the existing scalar-image curvature approach which makes use of the eigenvalues of the Hessian matrix [1]. In the case of vector-valued images, the Hessian is no longer a 2D matrix but rather a rank 3 tensor. We use quaternion curvature to derive vesselness measure for tubular structures in color or vector-valued images by extending Frangi’s [1] vesselness measure for scalar images. Experimental results show the effectiveness of quaternion color curvature in generating a vesselness map.

Color universal design: the selection of four easily distinguishable colors for all color vision types

Abstract: The objective of this project is to establish a practical application of the concept of Color Universal Design (CUD), the design that is recognizable to all color vision types. In our research, we looked for a clearly distinguishable combination of hues of four colors - black, red, green, and blue - which are frequently used in these circumstances. Red-green confusion people do not confuse all kinds of red and all kinds of green. By selecting particular hues for each color, the ability to distinguish between the four colors should be greatly improved. Our study thus concluded that, by carefully selecting hues within the range of each color category, it is possible to establish color-combinations which are easily distinguishable to people of all color-vision types in order to facilitate visual communication.

Adaptive sharpening of photos

Abstract: Sharpness is an important attribute that contributes to the overall impression of printed photo quality. Often it is impossible to estimate sharpness prior to printing. Sometimes it is a complex task for a consumer to obtain accurate sharpening results by editing a photo on a computer. The novel method of adaptive sharpening aimed for photo printers is proposed. Our approach includes 3 key techniques: sharpness level estimation, local tone mapping and boosting of local contrast. Non-reference automatic sharpness level estimation is based on analysis of variations of edges histograms, where edges are produced by high-pass filters with various kernel sizes, array of integrals of logarithm of edges histograms characterizes photo sharpness, machine learning is applied to choose optimal parameters for given printing size and resolution. Local tone mapping with ordering is applied to decrease edge transition slope length without noticeable artifacts and with some noise suppression. Unsharp mask via bilateral filter is applied for boosting of local contrast. This stage does not produce strong halo artifact which is typical for the traditional unsharp mask filter. The quality of proposed approach is evaluated by surveying observer's opinions. According to obtained replies the proposed method enhances the majority of photos.

Retinal HDR Images: Intraocular Glare and Object Size.

Abstract: — Starting from measured scene luminances, the retinal images of high-dynamic-range (HDR) test targets were calculated. These test displays contain 40 gray squares with a 50% average surround. In order to approximate a natural scene, the surround area was made up of half-white and half-black squares of different sizes. In this display, the spatial-frequency distribution approximates a 1/f function of energy vs. spatial frequency. Images with 2.7 and 5.4 optical density ranges were compared. Although the target luminances are very different, after computing the retinal image according to the CIE scatter glare formula, it was found that the retinal ranges are very similar. Intraocular glare strongly restricts the range of the retinal image. Furthermore, uniform, equiluminant target patches are spatially transformed to different gradients with unequal retinal luminances. The usable dynamic range of the display correlates with the range on the retina. Observers report that appearances of white and black squares are constant and uniform, despite the fact that the retinal stimuli are variable and non-uniform. Human vision uses complex spatial processing to calculate appearance from retinal arrays. Spatial image processing increases apparent contrast with increased white area in the surround. Post-retinal spatial vision counteracts glare.

Simplified ink spreading equations for CMYK halftone prints

Abstract: The Yule-Nielsen modified spectral Neugebauer model enables predicting reflectance spectra from surface coverages. In order to provide high prediction accuracy, this model is enhanced with an ink spreading model accounting for physical dot gain. Traditionally, physical dot gain, also called mechanical dot gain, is modeled by one ink spreading curve per ink. An ink spreading curve represents the mapping between nominal to effective dot surface coverages when an ink halftone wedge is printed. In previous publications, we have shown that using one ink spreading curve per ink is not sufficient to accurately model physical dot gain, and that the physical dot gain of a specific ink is modified by the presence of other inks. We therefore proposed an ink spreading model taking all the ink superposition conditions into account. We now show that not all superposition conditions are useful and necessary when working with cyan, magenta, yellow, and black inks. We therefore study the influence of ink spreading in different superposition conditions on the accuracy of the spectral prediction model. Finally, we propose new, simplified ink spreading equations that better suit CMYK prints and are more resilient to noise.

Printer characterization for UV encryption applications

Abstract: The appearance of a color print viewed under UV illumination is predicted using a target comprising color patches each printed using a known coverage of printer colorant(s). In one case, the target is illuminated using a UV light source and an electronic image of the target is captured using a digital camera or the like. In another case, a spectrophotometer is used both with and without a UV cutoff filter to measure the target. The captured image data or the spectrophotometric measurements are used to derive a UV printer characterization model that relates any arbitrary combination of printer colorants to a predicted UV color appearance value. Metameric colorant mixture pairs for visible light and UV light viewing can be determined using the UV model together with a conventional visible light printer characterization model. A visual matching task is used to determine a correction factor for the UV printer characterization model.

The effect of image size on the color appearance of image reproductions

Abstract: Original and reproduced art are usually viewed under quite different viewing conditions. One of the interesting differences in viewing condition is size difference. The main focus of this research was investigation of the effect of image size on color perception of rendered images. This research had several goals. The first goal was to develop an experimental paradigm for measuring the effect of image size on color appearance. The second goal was to identify the most affected image attributes for changes of image size. The final goal was to design and evaluate algorithms to compensate for the change of visual angle (size). To achieve the first goal, an exploratory experiment was performed using a colorimetrically characterized digital projector and LCD. The projector and LCD were light emitting devices and in this sense were similar soft-copy media. The physical sizes of the reproduced images on the LCD and projector screen could be very different. Additionally, one could benefit from flexibility of soft-copy reproduction devices such as real-time image rendering, which is essential for adjustment experiments. The capability of the experimental paradigm in revealing the change of appearance for a change of visual angle (size) was demonstrated by conducting a paired-comparison experiment. Through contrast matching experiments, achromatic and chromatic contrast and mean luminance of an image were identified as the most affected attributes for changes of image size. Measurement of the extent and trend of changes for each attribute were measured using matching experiments. Proper algorithms to compensate for the image size effect were design and evaluated. The correction algorithms were tested versus traditional colorimetric image rendering using a paired-comparison technique. The paired-comparison results confirmed superiority of the algorithms over the traditional colorimetric image rendering for the size effect compensation.

Accuracy-Preserving Smoothing Of Color Transformation LUTs.

Abstract:  Color transformation using look-up tables(LUTs) – Based on color measurements – Noise in color measurements and device output • Lack of smoothness in color transitions • Necessity of smoothing process – Smoothing of color LUTs • Significant loss of accuracy  Proposed method – Smoothing LUTs and preserving a colorimetric accuracy • Smoothing along 1D paths where lightness change

Visual Equivalence: an Object-based Approach to Image Quality.

Abstract: In this paper we introduce a new approach to characterizing image quality: visual equivalence. Images are visually equivalent if they convey the same information about object appearance even if they are visibly different. In a series of psychophysical experiments we explore how object geometry, material, and illumination interact to produce images that are visually equivalent, and we identify how two kinds of transformations on illumination fields (blurring and warping) influence observers’ judgments of equivalence. We use the results of the experiments to derive metrics that can serve as visual equivalence predictors (VEPs) and we generalize these metrics so they can be applied to novel objects and scenes. Finally we validate the predictors in a confirmatory study, and show that they reliably predict observer’s judgments of equivalence. Visual equivalence is a significant new approach to measuring image quality that goes beyond existing visible difference metrics by leveraging the fact that some kinds of image differences do not matter to human observers. By taking advantage of higher order aspects of visual object coding, visual equivalence metrics should enable the development of powerful new classes of image capture, compression, rendering, and display algorithms. Introduction Measuring image differences is an important aspect of image quality evaluation, and a variety of metrics have been developed for this purpose. Numerical metrics measure physical differences between a reference image and test image and characterize quality in terms of the distance from the reference to the test. Well known numerical metrics include mean squared error (MSE) and peak signal to noise ratio (PSNR). Although these metrics are easy to compute, they often do not correlate well with observers’ judgments of image differences. For this reason, perceptual metrics have been developed that incorporate computational models of human visual processing. In these metrics visual models are used to represent an observer’s responses to the reference and test images and then these responses are compared to identify visible differences. Popular perceptual metrics include Daly’s Visible Differences Predictor (VDP) and the Lubin/Sarnoff model. These metrics typically do a better job at predicting the visual impact of common imaging artifacts such as noise and quantization on perceived image quality, and many researchers have successfully applied these perceptual metrics to important problems in digital imaging. However current metrics have an interesting limitation that is illustrated in Figure 1. Figure 1a and 1b show two computer-generated images of a tabletop scene. Figure 1a was rendered using path tracing, a physically accurate but computationally intensive algorithm that can produce faithful simulations of environmental light reflection and transport. It can take hours to render a single image using path tracing. In contrast, Figure 1b was rendered using environment mapping, a fast but approximate rendering technique that uses an image of the surround rather than the model of the surround to illuminate the objects on the tabletop. Environment mapping is a standard feature of commodity graphics hardware and can render images like the one shown in Figure 1b at interactive rates. One consequence the approximations used in environment mapping is that illumination features such as surface reflections are warped with respect to the geometrically correct features produced by path tracing. This can be seen by comparing the images reflected by the two teapots. If we take the path traced image as the reference, and the environment mapped image as the test, and process the images with one of the standard perceptual metrics (in this case an implementation of Daly’s VDP), the metric produces the difference map shown in Figure 1c which correctly indicates that the images are visibly different (green and red pixels 75% and 95% probability of detection respectively). However an important question is: are these meaningful image differences? When we look at images we don’t see pixels. Rather, we see objects with recognizable shapes, sizes, and materials, at specific spatial locations, lit by distinct patterns of illumination. From this perspective the two images shown in Figure 1 are much more similar than they are different. For example, the shapes, sizes, and locations of the objects shown in the images appear the same; the objects appear to have the same material properties; and the lighting in the scenes seems the same. Although the images are visibly different they are visually equivalent as representations of object appearance. The existence of images like these coupled with the growing range of image transformations used in computer graphics, digital imaging, and computational photography points to the need for a new kind of image difference/quality metric that can predict when different classes of imaging algorithms produce images that are visually equivalent. Figure 2: Factors that affect object appearance. Dynamics and viewpoint can also play significant roles. Understanding Object Appearance The concept of visual equivalence is based on the premise that two visibly different images can convey the same information about object appearance to a human observer. To develop metrics for predicting when images will be visually equivalent we need to understand the factors that influence object appearance. Figure 2 shows a computer-generated image of a chrome bunny. We perceive the properties of this and other objects based on the patterns of light they reflect to our eyes. For a fixed object and viewpoint these patterns are determined by the geometry of the object, its material properties, and the illumination it receives. The perception of each of these properties is the subject of an extensive literature that will only be briefly reviewed here. More comprehensive introductions on these subtopics are available in the papers cited. Shape perception: The central problem in shape perception is how the visual system recovers 3D object shape from the 2D retinal images. Many sources of information for shape have been identified including stereopsis, surface shading, shadows, texture, perspective, motion, and occlusion Recent work has tried to characterize the effectiveness of these different sources and to model how they combine to provide reliable shape percepts. Material perception: Although there is significant interest in industry on the topic of material perception, there has been relatively little basic research on the topic. This situation is changing with the development of advanced computer graphics techniques that allow the accurate simulation and systematic manipulation of realistic materials in complex scenes. Active research topics include the perception of 3D surface lightness and color, gloss perception, perception of translucency , and 3D texture appearance. Illumination perception: Historically, illumination has been regarded as a factor that needs to be discounted to achieve shape and lightness/color constancy, but recently there has been interest in understanding illumination perception itself. Recent studies include the characterization of natural illumination statistics and surface illuminance flow, the perception of illumination directionality and complexity, and tolerance for illumination inconsistencies. So an object’s appearance is based on the images it reflects to our eyes, and these images are determined by the object’s geometry, material, and illumination properties. How the visual system disentangles the image information to perceive these object properties is one of the great unsolved problems in vision research. Although eventually we would like to understand this process, the goal of this paper is more immediate: to develop metrics that can predict when visibly different images are equivalent as representations of object appearance. To achieve this goal conducted a series of experiments that investigated when different configurations of object geometry, material, and illumination produce visually equivalent images. Experiments Even for a single object, the space of images spanned by all possible variations in object geometry, material properties, and scene illumination is vast. To begin to quantify the phenomenon of visual equivalence we had to constrain the scope of our studies. Starting from the proof-of-concept demonstration shown in Figure 1, we decided to study visual equivalence across two kinds of illumination transformations (blurring and warping) for objects with different geometric and material properties. The following sections describe our methods and procedures. Stimuli To conduct the experiments we created a set of images that Figure 3. The geometries and materials of the objects used in the experiments. Parameters were chosen to be perceptually uniform in both surface “bumpiness” and surface reflectance. would allow us to systematically explore the interactions between object geometry, material, and illumination. To accomplish this we used computer-generated images. Figure 3 shows representative images from our stimulus set. The scene consisted of a bumpy ball-like test object on a brick patio flanked by two pairs of children's blocks. The following paragraphs describe the parameters we used to generate the images. Geometry: The four object geometries (G0-G3) shown in the rows of Figure 3 were defined as follows. Object G0 was a geodesically tesselated sphere with 164 vertices. Objects G1 though G3 were generated by placing the sphere in a cube of Perlin noise and displacing the vertices according to the 3d noise function. By varying the size of the cube relative to the sphere (scale factors of 2,1, 1/2,1/2, 1/8 respectively) it was possible to produce random surface displacements that were constant in amplitude but varied in spatial frequency bandwidth. In pre-testing the objects were informally judged to be

Automatic red eye correction and its quality metric

Abstract: The red eye artifacts are troublesome defect of amateur photos. Correction of red eyes during printing without user intervention and making photos more pleasant for an observer are important tasks. The novel efficient technique of automatic correction of red eyes aimed for photo printers is proposed. This algorithm is independent from face orientation and capable to detect paired red eyes as well as single red eyes. The approach is based on application of 3D tables with typicalness levels for red eyes and human skin tones and directional edge detection filters for processing of redness image. Machine learning is applied for features selection. For classification of red eye regions a cascade of classifiers including Gentle AdaBoost committee from Classification and Regression Trees (CART) is applied. Retouching stage includes desaturation, darkening and blending with initial image. Several versions of approach implementation using trade-off between detection and correction quality, processing time, memory requirements are possible. The numeric quality criterion of automatic red eye correction is proposed. This quality metric is constructed by applying Analytic Hierarchy Process (AHP) to consumer opinions about correction outcomes. Proposed numeric metric helps to choose algorithm parameters via optimization procedure. Experimental results demonstrate high accuracy and efficiency of the proposed algorithm in comparison with existing solutions.

A smoothness metric for colour transforms

Abstract: Metrics are proposed which allow the evaluation of the intrinsic smoothness of an output colour transform One metric is based on taking the second derivative of the transform The second method computes a series of smooth gradients and evaluates the differences between these gradients and the actual output values A psychophysical experiment was performed to evaluate the proposed metrics, and both were found to be highly correlated with the observers' visual scale values

A Spatially AdaptiveWiener Filter for Reflectance Estimation.

Abstract: Wiener filtering has many applications in the area of imaging science. In image processing, for instance, it is a common way of reducing Gaussian noise. In color science it is often used to estimate reflectances from camera response data on a pixel by pixel basis. Based on a priori assumptions the Wiener filter is the optimal linear filter in the sense of the minimal mean square error to the actual data. In this paper we propose a spatially adaptive Wiener filter to estimate reflectances from images captured by a multispectral camera. The filter estimates pixel noise using local spatial neighborhood and uses this knowledge to estimate a spectral reflectance. In the hypothetical case of a noiseless system, the spatially adaptive Wiener filter equals the standard Wiener filter for reflectance estimation. We present results of various simulation experiments conducted on a multispectral image database using a 6-channel acquisition system and different noise levels. Introduction Estimation of reflectance spectra from camera responses is generally an ill-posed problem since a high dimensional signal is reconstructed from a relatively low dimensional signal. Associated with the development of multispectral camera systems many techniques were developed to tackle this problem. The basic approach to achieve a good estimation of scene reflectance spectra is to utilize as much information from the underlying capturing process as possible. Success is commonly evaluated through the spectral root mean square difference from the measured reflectance spectrum or color differences (e.g. CIEDE2000 [1]) for a set of selected illuminants. We will give a short and by far not exhaustive overview of reflectance reconstruction methods in the following text. Information used by reflectance estimation methods may include the knowledge of the acquisition illuminant, the channel sensitivities of the camera, noise properties of the system and a priori knowledge of the source reflectance. Most of the methods listed below require a priori knowledge of the spectral sensitivities of the camera system and of the acquisition illuminant. A common approach is to consider properties of natural reflectance spectra as additional a priori knowledge. These properties include positivity, boundedness and smoothness. Low effective dimensionality [2]. of natural reflectances is the reason why many methods use a low-dimensional linear model to describe spectra such as introduced in this context by Maloney and Wandell [3]. Some methods utilize a low dimensional linear model of reflectances to calculate the smoothest reflectance of all device metameric spectra (all spectra that lead to the given sensor response)[4, 5, 6]. Other methods use nearest neighbor type approaches within higher dimensional linear models [7] or adaptive principle component analysis (PCA) [8]. It was observed that a combination of multiple techniques can lead to improved reconstructions [9]. DiCarlo and Wandell extended the linear model in order to find reflectances lying on a submanifold that may describe the set of captured reflectances more accurate [10]. When camera sensitivities are not available, some approaches treat the system as a black box and use captured color-targets with known reflectances in order to construct a response-to-reflectance transformation [11, 12, 13]. The accuracy of these target-based methods is by construction highly dependent on the training target [14]. If additional information about the captured spectra is known, e.g. by a low resolution spectral sampling of the image [15] or by capturing printed images knowing the model of the printing device [16, 17], the accuracy of the spectral reconstruction can be further improved. A special linear estimation technique widely used in spectral reconstruction is the Wiener filter. Based on the assumption of a normal distribution of reflectances and system noise and the assumption that noise is statistically independent of the reflectances, it is the optimal linear filter in the sense of the minimal mean square error to the actual reflectance. The Wiener filter has the form r = KrΩ (ΩKrΩ +Ke )−1c (1) where Kr is the covariance matrix of reflectance spectra, Ke is the covariance matrix of additive noise, c is the sensor response, Ω is the device lighting matrix described in detail in eq. (2) and r is the reconstructed spectrum. Several factors can prevent the Wiener filter from performing optimally in the sense of the minimal mean square error: 1. The reflectance covariance matrix Kr can only be approximated suboptimally. A minimal knowledge approach uses a Toeplitz matrix [18]. Other approaches use a representative set of reflectances to estimate the covariance matrix [19]. Shen and Xin [20, 21] proposed a method that adaptively selects and weighs these training spectra in order to estimate the reflectance covariance matrix based on the actual sensor response. A Bayesian approach of Zhang and Brainard highly related to the Wiener filter estimates the covariance 16th Color Imaging Conference Final Program and Proceedings 279 matrix using a low dimensional space of reflectance weights [22]. 2. The Wiener filter cannot ensure the positivity and boundedness of the estimation. Both are important properties of natural reflectances. In the approach of Zhang and Brainard [22] that performs a Gaussian fit in a low dimensional space of reflectance weights all weights that correspond to reflectance functions with negative values have been excluded. This technique shall ensure the positivity of reconstructions. 3. Noise plays an important role in image acquisition systems. The accuracy of the Wiener estimation is highly dependent on the magnitude of system noise. Furthermore, the Wiener filter assumes signal-independent noise and disregards the signal-dependent shot noise. In eq. (3) the noise sources in electronic imaging devices are sketched and a commonly used noise model is introduced. An additional problem is the estimation of the noise covariance matrix. The accuracy of the Wiener reconstruction is highly dependent on the quality of the noise covariance estimation. Shimano [23] proposed a method for estimating this noise covariance matrix and achieved a good performance in terms of colorimetric and spectral RMS errors compared to multiple methods described above [24]. The first two problems above are not addressed in this paper. If desired, the algorithms proposed by other authors can be incorporated to the proposed spatially adaptive Wiener filter. Our paper focuses solely on the noise problem. The observation of the large dependency of the Wiener estimation on the magnitude of the noise variance leads to the idea of reducing noise based on the local pixel neighborhood in order to improve the reconstruction of the Wiener filter. The idea of combining spectral reflectance reconstruction with spatial noise reduction is not new. In a recent article Murakami et al. [25] proposed a spatio-spectral Wiener filter, which was called 3D Wiener (merging of 2 spatial dimension and 1 spectral dimensions in a single Wiener filter). In Murakami’s et al. article a sequence of spatial Wiener filtering followed by spectral Wiener filtering was investigated as well and called 2D+1D Wiener filter. The 2D Wiener filter is applied channel-wise on the sensor-response image. The noise variance is estimated globally from the resulting image and used in the subsequent spectral 1D Wiener filter. The approach proposed in this paper goes beyond 2D+1D Wiener filtering but does not go far as 3D Wiener filtering. In contrast to Murakami’s et al. 2D+1D approach our 2D noise reduction is performed on all channels simultaneously using a single Wiener filter. The noise covariance matrix is updated locally and propagated to the spectral Wiener filter. Both steps can be combined as a single operator, which enables a simple parallel computing, similar to Murakami’s et al. 3D Wiener filter. We will derive the spatially adaptive Wiener filter by Bayesian inference. Its noise reduction and propagation properties will be especially emphasized. Model of a Linear Acquisition System In this paper we consider linear acquisition systems. The discrete model of a n-channel capturing system is given by the following formula

An inverse display color characterization model based on an optimized geometrical structure

Abstract: This is the copy of journal's version originally published in Proc. SPIE 6807. Reprinted with permission of SPIE: http://spie.org/x10.xml?WT.svl=tn7

Controlled and uncontrolled viewing conditions in the evaluation of prints

Abstract: Visual experiments, attesting visual preference, visual ranking and visual differentiation, are very important to academia and industry. They are traditionally performed into laboratories under controlled viewing conditions, resulting very costly in their execution, due to the time and effort involved by all participants. If controlled tests could be substituted by uncontrolled tests, a potential serious improvement could be obtained by eliminating a large part of the cost. In this work we investigate if, and to what extent, visual experiments performed under controlled viewing conditions can be substituted by uncontrolled experiments. A task of visual preference of prints is carried out. This task is performed in the laboratory, under controlled viewing conditions, and in many different places, under natural, artificial and mixed light. We observe statistical equivalence for preferences expressed in controlled or uncontrolled conditions that supports the hypothesis that visual preference can be assessed with uncontrolled tests.

Perceptually optimal boundaries for wide gamut TVs

Abstract: Displays are coming on the market that can produce a larger range of colors over EBU and this has led to much research on the topic of how to use the additional color gamut volume provided by the displays. Some research has focused on different methods to map colors from small to large gamuts, whereas this paper focuses on defining the required gamut boundaries for natural content. Two gamuts were created from the results of a psychophysical experiment that asked observers to choose their preferred image in terms of saturation. One gamut corresponded to the median value of their choices and the second gamut corresponded to the 90% value of their choices. The results indicated that even at the 90% value, the resulting gamut was smaller than that of the wide-gamut display for most hues and actually closer to EBU for some hues. These results are display independent, at least when considering modern displays that can reach luminance values above 250cd/m2.© (2008) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Spectral prediction model for variable dot-size printers

Abstract: By printing a variable number of droplets onto the same pixel location, ink jet printers produce pixels at variable dot-s izes yielding several darkness levels. Varying the number of printed d roplets affects the ink volume deposited onto the substrate. In the p contribution, we explore the possibility of producing accu rate spectral reflectance predictions at all pixel dot-sizes. For this purpose, we use a Clapper-Yule model, extended according to Beer’s la w, which accounts for ink thickness variations. This model exp resses each colorant transmittance as a function of its constituen t ink transmittances and their respective relative thicknesses. Thes e relative thicknesses are initially computed when calibrating the mo del, at a given pixel dot-size, and can then be dynamically scaled ac cording to the printed pixel dot-size. We first study the effect ofvarying pixel dot-sizes on the halftone’s physical (mechanical) do t-gain. We then express the ink volume variations as a function of pixeldotsizes. Lastly, we show how, using the thickness extended Cla pperYule model, we can effectively predict reflectances for diff erent configurations of ink pixel dot-sizes.

Clustered-Dot Color HalftoneWatermarks.

Abstract: Spatial frequency separability is proposed as an attractive exploit for obtaining color halftone watermarking methods from monochrome clustered-dot halftone watermarking techniques. Detection of watermarks embedded in individual halftone channels is typically confounded by the cross-coupling between colorant halftone separations and scan RGB channels caused by the so-called “unwanted absorptions”. This problem is resolved in the proposed framework by utilizing spatial filtering in order to obtain estimates of individual separation halftones that are suitable for watermark detection. The effectiveness of this methodology is experimentally demonstrated by utilizing continuous phase modulation for per-separation watermark embedding. The embedded watermark patterns in the halftone separations can be clearly detected from scans using the proposed spatial separability exploit. Continuous phase modulation based per-channel embedding with detection following spatial-filtering based separation thus provides an effective watermarking method for clustered-dot color halftones.

Surface reflection properties of oil paints under various conditions

Abstract: This paper describes a method for measurement and analysis of surface reflection properties of oil paints under a variety of conditions. First, the radiance factor of a painting surface is measured at different incidence and viewing angles by using a gonio-spectro photometer. The samples are made from different oil paint materials on supporting boards with different paint thicknesses. Next, typical reflection models are examined for describing 3D reflection of the oil painting surfaces. The models are fitted to the observed radiance factors from the oil paint samples. The Cook- Torrance model describes well the reflection properties. The model parameters are estimated from the least-squared fitting to the genio-photometric measurements. Third, the reflection properties are analyzed on the basis of several material conditions such as pigment, supporting material, oil quantity, paint thickness, and support color.

Coding efficiency of CIE color spaces

Abstract: Estimates were made of the efficiency with which color spaces code color information from images of natural scenes. Six spaces were tested, namely, CIE XYZ tristimulus space, and the spaces CIELUV, CIELAB, CIELAB and S-CIELAB after chro-matic adaptation with CMCCAT2000, and the space CIECAM02. For each space, the information available and the information retrieved in color matching were calculated for images of 50 nat-ural scenes under different daylight illuminants. The information available was decomposed into components associated with the individual variables of the space and the interactions between them, including redundancy and illuminant-dependence. It was found that the information retrieved was much less than the in-formation available, and that its decomposition depended on the space. The differing efficiencies of the spaces were interpreted in relation to the effectiveness of opponent-color and chromatic-adaptation transformations, and the statistics of images of natural scenes.