Because of the emergence of e-commerce and developments in print engines designed for economical output of very short runs, there are increased business opportunities and consumer options for print-on-demand books and photobooks. The current state of these printing modes allows for direct uploading of book files via the web, printing on nonoffset printers, and distributing by standard parcel or mail delivery services. The goal of this research is to assess the image quality of print-on-demand books and photobooks produced by various Web-based vendors and to identify correlations between psychophysical results and objective metrics. Six vendors were identified for one-off (single-copy) print-on-demand books, and seven vendors were identified for photobooks. Participants rank ordered overall quality of a subset of individual pages from each book, where the pages included text, photographs, or a combination of the two. Observers also reported overall quality ratings and price estimates for the bound books. Objective metrics of color gamut, color accuracy, accuracy of International Color Consortium profile usage, eye-weighted root mean square L*, and cascaded modulation transfer acutance were obtained and compared to the observer responses. We introduce some new methods for normalizing data as well as for strengthening the statistical significance of the results. Our approach includes the use of latent mixed-effect models. We found statistically significant correlation with overall image quality and some of the spatial metrics, but correlations between psychophysical results and other objective metrics were weak or nonexistent. Strong correlation was found between psychophysical results of overall quality assessment and estimated price associated with quality. The photobook set of vendors reached higher image-quality ratings than the set of print-on-demand vendors. However, the photobook set had higher image-quality variability.
Spectral separation is the process of obtaining printer control values to reproduce a given spectral reflectance. Given a multispectral image where each pixel represents a spectral reflectance, separation could be implemented by inverting a physical printer model on a pixel-by-pixel basis. Such a process would obviously need to be very fast to handle high-resolution images in a reasonable time. For a printer whose spectral response is characterized by the Yule–Nielsen spectral Neugebauer model, the linear regression iteration (LRI) method can be used to invert the model. We introduce the subspace linear regression iteration (SLRI) method, a modification of LRI shown to be significantly accelerated due to performing its calculations within the subspace determined by the Neugebauer primaries. Using this subspace approach, the number of multiplications becomes independent of the spectral sampling rate. Using a standard six color printer and a common spectral sampling rate, the number of multiplications can be decreased by about two-thirds without changing the convergence behavior.
LCD televisions have LC response times and hold-type data cycles that contribute to the appearance of blur when objects are in motion on the screen. New algorithms based on studies of the human visual system's sensitivity to motion are being developed to compensate for these artifacts. This paper describes a series of experiments that incorporate eyetracking in the psychophysical determination of spatio-velocity contrast sensitivity in order to build on the 2D spatiovelocity contrast sensitivity function (CSF) model first described by Kelly and later refined by Daly. We explore whether the velocity of the eye has an additional effect on sensitivity and whether the model can be used to predict sensitivity to more complex stimuli. There were a total of five experiments performed in this research. The first four experiments utilized Gabor patterns with three different spatial and temporal frequencies and were used to investigate and/or populate the 2D spatio-velocity CSF. The fifth experiment utilized a disembodied edge and was used to validate the model. All experiments used a two interval forced choice (2IFC) method of constant stimuli guided by a QUEST routine to determine thresholds. The results showed that sensitivity to motion was determined by the retinal velocity produced by the Gabor patterns regardless of the type of motion of the eye. Based on the results of these experiments the parameters for the spatio-velocity CSF model were optimized to our experimental conditions.
All imaging devices have two gamuts: the stimulus gamut and the response gamut. The response gamut of a print engine is typically described in CIE colorimetry units, a system derived to quantify human color response. More fundamental than colorimetric gamuts are spectral gamuts, based on radiance, reflectance or transmittance units. Spectral gamuts depend on the physics of light or on how materials interact with light and do not involve the human's photoreceptor integration or brain processing. Methods for visualizing a spectral gamut raise challenges as do considerations of how to utilize such a data-set for producing superior color reproductions. Recent work has described a transformation of spectra reduced to 6-dimensions called LabPQR. LabPQR was designed as a hybrid space with three explicit colorimetric axes and three additional spectral reconstruction axes. In this paper spectral gamuts are discussed making use of LabPQR. Also, spectral gamut mapping is considered in light of the colorimetric-spectral duality of the LabPQR space.
Efforts to construct end-to-end color reproduction systems based on the preservation of scene spectral data have been underway at the Munsell Color Science Laboratory. The goal is to present hardcopy results which are spectrally matched to original colors. The evaluated approach consists of capturing scenes through a trichromatic digital camera combined with multiple filterings followed by an image processing stage and then four-color printing. The acquisition end is designed to estimate original scene spectra on a pixel-by-pixel basis based on system characteristics which takes into account the camera sensitivities as modulated by the filterings followed by an image processing stage and then four-color printing. The acquisition end is designed to estimate original scene spectra on a pixel-by-pixel basis based on system characterizations which takes into account the camera sensitivities as modulated by the filterings an scene colorant make-up. The spectral-based printing used in this research is able to produce the least metameric reproduction to the original scene using a computationally feasible approach. Results show a system accuracy of mean (Delta) E*94 of 1.5 and spectral reflectance rms error of 0.9 percent.
Traditional image processing techniques used for 3- and 4- band images are not suited to the many-band character of spectral images. A sparse multi-dimensional lookup table with inter-node interpolation is a typical image processing technique used for applying either a known model or an empirically derived mapping to an image. Such an approach for spectral images becomes problematic because input dimensionality of lookup tables is proportional to the number of source image bands and the size of lookup table sis exponentially related to the number of input dimensions. While an RGB or CMY source images would require a 3D lookup table, a 31-band spectral image would need a 31-dimensional lookup table. A 31-dimensional lookup table would be absurdly large. A novel approach to spectral image processing is explored. This approach combines a low-cost spectral analysis followed by application of one from a set of low-dimensional lookup tables. The method is computationally feasible and does not make excessive demands on disk space or run-time memory.