An important step in the color image processing pipeline for modern digital cameras is the transform from camera response resulting from scene spectral radiance to objective colorimetric or related quantities. Knowledge of the camera spectral sensitivities is essential for building robust camera transforms for arbitrary capture and viewing conditions. Monochromator-based techniques are well-known but are cumbersome, slow, and impractical for routine use such as production-line camera calibration. It has been shown that with suitable characterization data it is possible to accurately estimate spectral sensitivities without the use of a monochromator. However, these methods are inherently highly metameric and the performance on likely scene capture spectra is unclear. In the current work, it is shown that combining highlydimensional characterization data with multidimensional analysis of typical camera responses produces spectral sensitivity estimates for a test camera that performs extremely well on likely scene spectra while minimizing the opportunity for camera observer metamerism.

In digital camera applications observed color is formed by light reflecting from or transmitted through a medium, observed by a sensor. The final digital value output by a camera is a composite of the spectrum of emitted light, reflectance (or transmittance) properties of a material and spectral response of the observing sensor. In this paper, we demonstrate how even small changes in any of the mentioned properties can cause clearly visible and sometimes unwanted changes in the digital numbers produced by a camera. Additionally, we explore this problem from the point-of-view of a 3D 360° camera, show how these problems are even more severe in this particular case as well as propose a solution relying on accurate measurement of both light as well as camera's response to it.

Smartphones have become ingrained in our daily activities, driving Smartphone cameras to become better with every generation. As more and more images are being taken by smartphones it has become increasingly important to assess the quality of the images taken by different phones. The Cell Phone Image Quality (CPIQ) Group created the IEEE P1858 CPIQ Standard. To subjectively validate the group's metric, psychophysical tests were performed; each tested observers' preferences for a wide range of images. While many smartphone images are only viewed electronically, many images also get transformed into printed images, especially photobooks, as digital printing gets better and cheaper compared to traditional printing processes. The main goals of this research were to evaluate the image quality of smartphone images, both electronically displayed and for several printers, and to compare print quality to displayed quality. The subjective results indicated that the perceived quality of images is well-correlated with the objective results of the IEEE P1858 CPIQ Standard. It was also found that smartphones have a bigger impact on the image quality compared to the digital printers.

Capsule endoscopy, using a wireless camera to capture the digestive track, is becoming a popular alternative to traditional colonoscopy. The images obtained from a capsule have lower quality compared to traditional colonoscopy, and high-quality images are required by medical doctors in order to set an accurate diagnosis. Over the last years several enhancement techniques have been proposed to improve the quality of capsule images. In order to verify that the capsule images have the required diagnostic quality some kind of quality assessment is required. In this work, the authors evaluate state-of-the-art no-reference image quality metrics for capsule video endoscopy. Furthermore, they use the best performing metric to optimize one of the capsule video endoscopy enhancement methods and validate through subjective experiment. © 2017 Society for Imaging Science and Technology.

Among optical properties required for characterizing visual appearance in any lighting and viewing conditions, the most fundamental ones play an important role in predictive rendering. Spectroscopic ellipsometry is useful to acquire the complex indices of refraction of any homogeneous material. A multiscale approach using fundamental optical properties that are the components of the complex dielectric tensor of all the material compounds acquired separately is illustrated using automotive paints as an example.

This paper proposes a method for reconstructing the scene appearance of fluorescent objects under different conditions of materials and light source. First, the observed spectral image of two fluorescent objects with the mutual illumination effect is described by the multiplication of the spectral functions and the geometric factors. Second, the observed image is decomposed into spectral component images by the ridge regression approach. Third, the geometric factor of the self-luminescent component is separated into the direct and indirect illumination components by taking the spatial distributions of reflection and interreflection into account. Then the scene appearance of the two fluorescent objects under different conditions is reconstructed using the five components of (1) reflection, (2) interreflection, (3) luminescence by direct illumination, (4) luminescence by indirect illumination, and (5) interreflection by fluorescent illumination. The feasibility is shown using two scenes consisting of two fluorescent objects.

In addition to colors and shapes, factors of material appearance such as glossiness, translucency, and roughness are important for reproducing the realistic feeling of images. In general, these perceptual qualities are often degraded when reproduced as digital color images. Therefore, it is useful to enhance and reproduce them. In this article, the authors propose a material appearance enhancement algorithm for digital color images. First, they focus on the change of pupil behaviors, which is the first of the early vision systems to recognize visual information. According to their psychophysiological measurement of pupil size during material observation, they find that careful observation of surface appearance causes the pupil size to contract further. Next, they reflect this property in the retinal response, which is the next system in early vision. Then, they construct a material appearance enhancement algorithm named "PuRet" based on these physiological models of pupil and retina. By applying the PuRet algorithm to digital color test images, they confirm that perceived material appearance, including glossiness, transparency, and roughness, in the images is enhanced by using their PuRet algorithm. Furthermore, they show possibilities to apply their algorithm to a material appearance management system that could produce equivalent appearance qualities among different imaging devices by adjusting one parameter of PuRet. © 2017 Society for Imaging Science and Technology.

Visual perception of materials that make up objects has been gaining increasing interest. Most previous studies on visual material-category perception have used stimuli with rich information, e.g., color, shape, and texture. This article analyzes the image features of the material representations in Japanese "manga" comics, which are composed of line drawings and are typically printed in black and white. In this study, the authors first constructed a manga-material database by collecting 799 material images that gave consistent material impressions to observers. The manga-material data from the database were used to fully train "CaffeNet," a convolutional neural network (CNN). Then, the authors visualized training-image patches corresponding to the top-n activations for filters in each convolution layer. From the filter visualization, they found that the filters reacted gradually to complicated features, moving from the input layer to the output layer. Some filters were constructed to represent specific features unique to manga comics. Furthermore, materials in natural photographic images were classified using the constructed CNN, and a modest classification accuracy of 63% was obtained. This result suggests that material-perception features for natural images remain in the manga line-drawing representations. © 2017 Society for Imaging Science and Technology.

Colour critical assessment in the graphic arts industry is performed by using D50 simulators that obey rigorous criteria stipulated in ISO 3664 for years. Here multichannel LED based viewing cabinets are increasingly used. However the impact of phosphor converted LED based lighting for general lighting applications in lieu with colour assessment in prepress and press environments can be critical. Incorrect lighting might destroy ISO 3664 conformance and lead to colour differences that are larger than the domain of application of the advanced colour tolerances equations. A user friendly collection of metameric pairs was designed to solicit actual visual differences under 10 typical LED-based luminaires and six ISO 3664 compliant cabinets. These findings were correlated with modern colour rendering metrics including the new CIE fidelity index. Finally colour rendering tolerances will be proposed for general lighting that is used to support colour critical viewing and to certify LED based luminaires.