The quality of ICC profiles with embedded look-up tables (LUTs) depends on multiple factors: 1. the accuracy of the optical printer model, 2. the exploitation of the available gamut combined with the quality of the gamut mapping approach encoded in the B2A-LUTs (backwards LUTs) and 3. the tonal smoothness as well color accuracy of the backwards LUTs. It can be shown that optimizing the smoothness of the LUTs comes at the expense of color accuracy and requires gamut reduction because of internal tonal edges. We present a method to optimize backwards LUTs of existing ICC profiles w.r.t accuracy, smoothness, gamut exploitation and mapping, which can be extended beyond color, e.g. to joint color and translucency backward LUTs. The approach is based on a perceptual difference metric that is used to optimize the LUT's tonal smoothness constrained to preserve both the accuracy of and the relationship between colors.

We present an efficient, purely geometric, algorithmic, and parameter free approach to improve surface quality and accuracy in voxel-controlled 3D printing by counteracting quantization artifacts. Such artifacts arise due to the discrete voxel sampling of the continuous shape used to control the 3D printer, and are characterized by low-frequency geometric patterns on surfaces of any orientation. They are visually disturbing, particularly on small prints or smooth surfaces, and adversely affect the fatigue behavior of printed parts. We use implicit shape dithering, displacing the part's signed distance field with a high-frequent signal whose amplitude is adapted to the (anisotropic) print resolution. We expand the reverse generalized Fourier slice theorem by shear transforms, which we leverage to optimize a 3D blue-noise mask to generate the anisotropic dither signal. As a point process it is efficient and does not adversely affect 3D halftoning. We evaluate our approach for efficiency, geometric accuracy and show its advantages over the state of the art.

Advances in multimaterial 3D printing have the potential to reproduce various visual appearance attributes of an object in addition to its shape. Since many existing 3D file formats encode color and translucency by RGBA textures mapped to 3D shapes, RGBA information is particularly important for practical applications. In contrast to color (encoded by RGB), which is specified by the object’s reflectance, selected viewing conditions, and a standard observer, translucency (encoded by A ) is neither linked to any measurable physical nor perceptual quantity. Thus, reproducing translucency encoded by A is open for interpretation. In this article, we propose a rigorous definition for A suitable for use in graphical 3D printing, which is independent of the 3D printing hardware and software, and which links both optical material properties and perceptual uniformity for human observers. By deriving our definition from the absorption and scattering coefficients of virtual homogenous reference materials with an isotropic phase function, we achieve two important properties. First, a simple adjustment of A is possible, which preserves the translucency appearance if an object is rescaled for printing. Second, determining the value of A for a real (potentially non-homogenous) material, can be achieved by minimizing a distance function between light transport measurements of this material and simulated measurements of the reference materials. Such measurements can be conducted by commercial spectrophotometers used in graphic arts. Finally, we conduct visual experiments employing the method of constant stimuli, and we derive from them an embedding of A into a nearly perceptually uniform scale of translucency for the reference materials.

We present an efficient and scalable pipeline for fabricating full-colored objects with spatially-varying translucency from practical and accessible input data via multi-material 3D printing. Observing that the costs associated with BSSRDF measurement and processing are high, the range of 3D printable BSSRDFs are severely limited, and that the human visual system relies only on simple high-level cues to perceive translucency, we propose a method based on reproducing perceptual translucency cues. The input to our pipeline is an RGBA signal defined on the surface of an object, making our approach accessible and practical for designers. We propose a framework for extending standard color management and profiling to combined color and translucency management using a gamut correspondence strategy we callopaque relative processing.We present an efficient streaming method to compute voxel-level material arrangements, achieving both realistic reproduction of measured translucent materials and artistic effects involving multiple fully or partially transparent geometries.

Accurate color reproduction is important in many applications of 3D printing, from design prototypes to 3D color copies or portraits. Although full color is available via other technologies, multi-jet printers have greater potential for graphical 3D printing, in terms of reproducing complex appearance properties. However, to date these printers cannot produce full color, and doing so poses substantial technical challenges, from the shear amount of data to the translucency of the available color materials. In this paper, we propose an error diffusion halftoning approach to achieve full color with multi-jet printers, which operates on multiple isosurfaces or layers within the object. We propose a novel traversal algorithm for voxel surfaces, which allows the transfer of existing error diffusion algorithms from 2D printing. The resulting prints faithfully reproduce colors, color gradients and fine-scale details.

We propose a new strategy to evaluate the quality of multi and hyperspectral images, from the perspective of human perception. We define the spectral image difference as the overall perceived difference between two spectral images under a set of specified viewing conditions (illuminants). First, we analyze the stability of seven image-difference features across illuminants, by means of an information-theoretic strategy. We demonstrate, in particular, that in the case of common spectral distortions (spectral gamut mapping, spectral compression, spectral reconstruction), chromatic features vary much more than achromatic ones despite considering chromatic adaptation. Then, we propose two computationally efficient spectral image difference metrics and compare them to the results of a subjective visual experiment. A significant improvement is shown over existing metrics such as the widely used root-mean square error.

While image-difference metrics show good prediction performance on visual data, they often yield artifact-contaminated results if used as objective functions for optimizing complex image-processing tasks. We investigate in this regard the recently proposed color-image-difference (CID) metric particularly developed for predicting gamut-mapping distortions. We present an algorithm for optimizing gamut mapping employing the CID metric as the objective function. Resulting images contain various visual artifacts, which are addressed by multiple modifications yielding the improved color-image-difference (iCID) metric. The iCID-based optimizations are free from artifacts and retain contrast, structure, and color of the original image to a great extent. Furthermore, the prediction performance on visual data is improved by the modifications.

We introduce a new strategy to evaluate the perceived quality of spectral image reproductions. We rely on the idea that spectral image difference can be computed by averaging scores of a color image-difference measure, computed for various viewing conditions. Two approaches are proposed to efficiently approximate this quality prediction by taking advantage of image-difference redundancies across illuminants. Our results suggest that, for the evaluated distortions, image-difference features extracted from the lightness component vary much less across illuminants than those from chroma and hue components. For our setup, we could reduce the numerical effort to compute the original prediction to 1.3%, without impairing accuracy.

Existing image-difference measures show excellent accuracy in predicting distortions, such as lossy compression, noise, and blur. Their performance on certain other distortions could be improved; one example of this is gamut mapping. This is partly because they either do not interpret chromatic information correctly or they ignore it entirely. We present an image-difference framework that comprises image normalization, feature extraction, and feature combination. Based on this framework, we create image-difference measures by selecting specific implementations for each of the steps. Particular emphasis is placed on using color information to improve the assessment of gamut-mapped images. Our best image-difference measure shows significantly higher prediction accuracy on a gamut-mapping dataset than all other evaluated measures.

Image processing methods that utilize characteristics of the human visual system require color spaces with certain properties to operate effectively. After analyzing different types of perception-based image processing problems, we present a list of properties that a unified color space should have. Due to contradictory perceptual phenomena and geometric issues, a color space cannot incorporate all these properties. We therefore identify the most important properties and focus on creating opponent color spaces without cross contamination between color attributes (i.e., lightness, chroma, and hue) and with maximum perceptual uniformity induced by color-difference formulas. Color lookup tables define simple transformations from an initial color space to the new spaces. We calculate such tables using multigrid optimization considering the Hung and Berns data of constant perceived hue and the CMC, CIE94, and CIEDE2000 color-difference formulas. The resulting color spaces exhibit low cross contamination between color attributes and are only slightly less perceptually uniform than spaces optimized exclusively for perceptual uniformity. We compare the CIEDE2000-based space with commonly used color spaces in two examples of perception-based image processing. In both cases, standard methods show improved results if the new space is used. All color-space transformations and examples are provided as MATLAB codes on our website.

A spectral agreement between the original scene and a printed reproduction is required to achieve an illuminant-invariant visual match. This is usually impossible since the spectral gamut of typical printing systems is only a small subset of all natural reflectances. Out-of gamut reflectances need to be mapped into the spectral gamut of the printer minimizing the perceived error between original and reproduction for more than one illuminant. In this paper, we propose an algorithmic framework for spectral gamut mapping to achieve a reproduction that is as visually correct as a colorimetric reproduction for one illuminant and is superior for a set of other illuminants. A sequence of hierarchical mappings in 3-D color spaces are performed utilizing the observer's color quantization to increase the spectral variability of subsequent transformations: For the most important illuminant a traditional colorimetric gamut mapping is performed. For any additional illuminants colors are mapped onto pixel-dependent paramer mismatch gamuts preserving the visual equivalence of previous transformations. We present a separation method for investigating the spectral gamut mapping framework and show that hue shifts and chroma gains cannot be always avoided for the second and subsequent illuminants and that the order of illuminants has a large impact on the final reproduction.