Seeing 600th anniversary of Gutenberg's birth in 2000 A.D., we should look back the historical significance of letterpress technology and take a step forward into color imaging new age. Now digital imaging technology plays a leading role in visual communication, but meets severe assessment to satisfy human vision. Software on “What's human vision seeing?” is essential to capture, store, transmit, and reproduce a truly realistic image just as human vision seeing. Not only advances in high precision and high definition digital media, but also Intelligent Image Processing technologies will be helpful for more aesthetic and pleasant imaging. We are approaching towards this direction from a stance of engineering to use the scientific results in human vision research. This paper introduces just a little “intelligent” processing to “image sharpening”, “local contrast enhancement”, and “color transform” by region-based, spatially-variant, and scene-referred approaches.

Two psychophysical experiments were performed to evaluate image preference of 6 high-dynamic-range (HDR) image rendering algorithms. The experiments were split into a paired comparison experiment examining overall preference, and a rating scale experiment judging individual preference for 6 image attributes: highlight details, shadow details, overall contrast, sharpness, colorfulness and artifacts. The paired comparison experiment was analyzed using Thurstone's law to generate interval scales. In addition, dual scaling analysis indicates a single perceptual dimension accounting for the variance of overall preference. The overall preference shows high correlations with shadow details, overall contrast, sharpness and colorfulness, which represent the most important factors in observers' preference judgment. Stepwise regression of various image attributes to the overall preference results showed that for many images the preference scales of a single attribute can predict the overall image preference.

A framework for evaluation of HDR video sequence rendering is proposed. We present the signal processing flow of an experimental configuration in which we placed a calibrated CRT monitor next to the HDR display to allow side-by-side evaluations. The HDR display was built using off-shelf components: a LCD panel and a DLP (Digital Light Projector). HDR images consisting of XYZ tristimulus images are converted to six channel images using HDR display characterization. The same HDR images in XYZ format can be tone mapped and rendered to the CRT display. Colorimetric measurement of random verification targets was taken from both calibrated displays and standard images were visually examined as well. Furthermore, we also evaluated how colorimetrically accurate is the image reproduction on HDR display compared to an original HDR scene. Based on the accuracy of the measurements and the visual match for the images we can conclude that we have a reasonably colorimetric accurate system to evaluate tone mapping algorithms. The specification of a computer system with processing and displaying capability for HDR video sequences is also presented showing specifications of what is required to perform this evaluation. We expect this framework to be useful as a reference to everyone trying to evaluate the accuracy of tone mapping for both HDR still image and image sequences.

A chromagenic camera takes two pictures of each scene. The first is taken as normal but a specially chosen coloured filter is placed in front of the camera when capturing the second image. The chromagenic filter is chosen so that the combined image makes colour constancy, or white point estimation easier to solve. The chromagenic illuminant estimation algorithm is very simple. We compute the expect relations, currently implemented as 3x3 matrix transforms, between unfiltered and filtered RGBs for a range of typical lights. These relations are tested in situ for a given chromagenic image and the one that best predicts the image data is used to designate the illuminant colour.However, in experiments we found that a 3x3 matrix transform, while generally quite accurate, can fail to model the relationship between filtered and unfiltered RGBs for some colours (e.g. saturated colours) and so, the chromagenic algorithm which works very well on average can nevertheless, on occasion, work poorly. In this paper we assume that convex combinations in local areas of RGB space are translated to the same convex combinations for corresponding filtered RGBs and use this insight to relate filtered and unfiltered RGBs. These locally convex relations model the image data more accurately. Testing these relations in situ in images and choosing the one which best models the data provides surprisingly effective illuminant estimation algorithm.Experiments demonstrate that the chromagenic colour constancy algorithm provides superior illuminant estimation compared with conventional approaches (Gamut mapping, color by correlation, max RGB etc). This result holds across many different data sets. The method is also demonstrated to work on real images. The plausibility of the chromagenic approach for human vision is also discussed.

A digital imaging system containing a calibration target, an image capture device, and a mathematical model to estimate spectral reflectance factor was treated as a spectrophotometer and as such subject to systematic and random errors. The systematic errors considered were photometric zero, photometric linear and nonlinear scale, wavelength linear and nonlinear scale, and bandwidth. To diagnose and correct the systematic errors in a spectral imaging system, a technique using multiple linear regression as a function of wavelength was employed, based on the measurement and image based estimating of several image verification targets. Based on the stepwise regression technique, the most significant diagnosed systematic errors were photometric zeros, photometric linear scale, wavelength linear scale, and bandwidth errors. The performance of spectral imaging after correction of the estimated spectral reflectance, based on the modeling result, was improved on average 25.3% spectrally and 16.7% colorimetrically. This technique is suggested as a general method to improve the performance of spectral imaging systems.

In the present paper, we propose a technique to control the appearance on an object in real time using a high-luminance PC projector and graphics hardware. We have previously proposed an image projection technique to reproduce the appearance of a real object on a mock object using a high-luminance projector. By controlling the projected image, the reflected radiance on the mock object is matched to that on the real object. However, in our previous study, only preliminary experiments were performed by matching the appearance empirically. The present paper uses ray tracing to reproduce the distribution of the reflected radiance for appearance matching. It is necessary to measure the bi-directional reflectance distribution function of the objects and the geometry between the projector and the observer's eyes. Since the observer's eyes move with the head in evaluating the appearance of the object, we performed a real-time reproduction of gloss appearance with the movement of the observer's position. The observer's position is detected by an electromagnetic position sensor. Graphics hardware is used in the real-time reproduction to calculate the ray tracing at high speed and render the appearance according to the observer's eye position in real time. Observer rating revealed that little difference in appearance was perceived between the real object and the projected mock object.

Spectral imaging has been widely developed over the last ten years for archiving cultural heritage. It can retrieve spectral reflectance of each scene pixel and provide the possibility to render images for any viewing condition. A new spectral reconstruction method, the matrix R method, can achieve high spectral and colorimetric accuracies simultaneously for a specific viewing condition. Although the matrix R method is very effective, the reconstructed reflectance spectrum is not smooth when compared with in situ spectrophotometry. The goal of this research was to smooth the spectrum and make it more accurate. One possible solution is to identify pigments and find their compositions for each pixel. After that, the reflectance spectrum can be modified based on two-constant Kubelka-Munk theory using the absorption and scattering coefficients of these pigments, weighted by their concentrations. The concentrations were optimized to best fit the spectral reflectance predicted by the matrix R method. As a preliminary experiment, it was assumed that a custom target was painted using several known pigments. The simulation results show that incorporating pigment mapping into the matrix R method can recover the smoothness of the reflectance spectrum, and further improve spectral accuracy of spectral imaging.

Quantitative characterization of skin appearance is an important but difficult task. Skin appearance is strongly affected by the direction from which it is viewed and illuminated. Computational modeling of surface texture has potential uses in many applications including realistic rendering for computer graphics and robust recognition for computer vision. For recognition, the overall structure of the object is important, but fine-scale details can assist the recognition problem greatly. We develop models of surface texture and demonstrate their use in recognition tasks. We also describe a texture camera for capturing fine-scale surface details. Specifically, the texture camera measures reflectance and surface height variation using curved mirrors. We discuss why measurements and models of fine scale detail are important in dermatology applications.

This paper presents an experimental investigation of the application of multispectral imaging in dermatology. The focus areas of this work are as follows: a) the improving the color reproduction accuracy of skin lesions, b) exploring the spectral feature of skin disease using the multispectral color enhancement technique, and c) multispectral image analysis aiming at supporting quantitative diagnosis. The experiment focused on inflammatory and immunologic diseases; the color of skin lesions associated with these diseases is believed to be difficult to reproduce by conventional imaging devices. In view of this fact, we demonstrate the effectiveness of using spectral information for the color reproduction and quantitative analysis of skin disorders.

In this study, various whiteness and yellowness indices were compared with regard to their ability to measure the perceived whiteness of human teeth. Psychophysical experiments were conducted by 80 observers on tooth whiteness perception under typical clinical test conditions. The Pearson correlation coefficient and ‘per-cent-wrong decision’ criterion were used to determine the best index for tooth whiteness measurement. The results confirmed the findings of a previous study that a modified form of the CIE Whiteness Index formula is appropriate for the prediction of tooth whiteness.