A method for preferred color reproduction with a psychophysical study based upon a real-life scene demonstrated application is presented. A color correction matrix optimization algorithm is first introduced which applies additional hue constraints on top of the CIE delta E magnitude error to emphasize the importance of hue in the subjective quality of color reproduction. An application is implemented by applying matrices optimized for the three dominant memory colors: skin, sky and green based on each pixel's categorical classification which is obtained through an explicitly defined boundary model. A psychophysical study was carried out to systematically investigate observers' hue preference in terms of the reproduction of individual memory colors as well as when multiple memory colors exist in the scene simultaneously. Conclusions suggest that observers' preference for individual memory colors is consistent with regard to scene composition. When a scene with multiple memory colors is evaluated, skin is given the highest priority in the overall preference scale, followed by sky and then green. In practice, the optimized preferred color reproduction may be achieved by first locating the matrices for individual memory colors through a rank order study, and then applying them based on each pixel's classification.
A standard practice in high dynamic range imaging is to compose an image through exposure bracketing which captures
a series of exposures of the same scene and then combine them together, followed by dynamic rang compression and
some color processing steps. Scenes lit by multiple illuminants such as a room with an artificial light source when the
sun is shining through the window is an often encountered scenario which offers opportunity for the high dynamic range
feature of an image pipeline to show its advantages. Traditional color constancy algorithms estimate a global white point
of the scene and then apply color correction based on this estimate, which could exaggerate the difference between the
illuminants, making part of the image better and part of the image worse, or compromise the color of the whole scene.
In this paper, we propose a method for the color constancy of high dynamic range scenes with multiple illuminants
utilizing the inherent difference in their luminance levels to assist the segmentation of the image into differently
illuminated portions and apply their corresponding color constancy parameters. Experimental results using two
exposures show superior performance of the proposed algorithm compared to traditional algorithms applying global
corrections only.
KEYWORDS: RGB color model, Image quality, Skin, Color reproduction, Cameras, Image processing, Data modeling, Digital imaging, Imaging systems, Visualization
Previous work has suggested that observers are capable of judging the quality of an image without any knowledge of the
original scene. When no reference is available, observers can extract the apparent objects in an image and compare them
with the typical colors of similar objects recalled from their memories. Some generally agreed upon research results
indicate that although perfect colorimetric rendering is not conspicuous and color errors can be well tolerated, the
appropriate rendition of certain memory colors such as skin, grass, and sky is an important factor in the overall perceived
image quality. These colors are appreciated in a fairly consistent manner and are memorized with slightly different hues
and higher color saturation.
The aim of color correction for a digital color pipeline is to transform the image data from a device dependent color
space to a target color space, usually through a color correction matrix which in its most basic form is optimized through
linear regressions between the two sets of data in two color spaces in the sense of minimized Euclidean color error.
Unfortunately, this method could result in objectionable distortions if the color error biased certain colors undesirably.
In this paper, we propose a color correction optimization method with preferred color reproduction in mind through hue
regularization and present some experimental results.
The under constrained nature of illuminant estimation determines that in order to resolve the problem, certain
assumptions are needed, such as the gray world theory. Including more constraints in this process may help explore the
useful information in an image and improve the accuracy of the estimated illuminant, providing that the constraints hold.
Based on the observation that most personal images have contents of one or more of the following categories: neutral
objects, human beings, sky, and plants, we propose a method for illuminant estimation through the clustering of pixels of
gray and three dominant memory colors: skin tone, sky blue, and foliage green. Analysis shows that samples of the
above colors cluster around small areas under different illuminants and their characteristics can be used to effectively
detect pixels falling into each of the categories. The algorithm requires the knowledge of the spectral sensitivity response
of the camera, and a spectral database consisted of the CIE standard illuminants and reflectance or radiance database of
samples of the above colors.
Bayer patterns, in which a single value of red, green or blue is available for each pixel, are widely used in digital color
cameras. The reconstruction of the full color image is often referred to as demosaicking. This paper introduced a new
approach - morphological demosaicking. The approach is based on strong edge directionality selection and interpolation,
followed by morphological operations to refine edge directionality selection and reduce color aliasing. Finally
performance evaluation and examples of color artifacts reduction are shown.
KEYWORDS: Digital imaging, Visual system, Cameras, High dynamic range imaging, CRTs, LCDs, Color difference, Contrast sensitivity, Printing, Image processing
High dynamic compression of natural scenes and nonlinearity compensation of output devices are demanded as the
output devices have limited dynamic range and color gamut, and devices such as Standard-RGB compliant displays and
printers have different nonlinear responses to linear inputs. This paper describes a general framework of nonlinear curve
optimization in digital imaging and its application to the determination of gamma look up table through curve
parameterization. Three factors are considered, that is, the color appearance fidelity, the feature preservation, and the
suppression of noise propagation.
KEYWORDS: Image quality, Colorimetry, Imaging systems, Image quality standards, Data modeling, Digital cameras, Image analysis, CRTs, Color reproduction, Digital imaging
Image noise is one of the important image quality metrics for evaluation and optimization in color reproduction system. While the noise evaluation in psychometric lightness L* works pretty well in black-and-white images, it is insufficient for color images. A perceptual color noise evaluation equation was derived to extend noise evaluation in CIELAB color space, incorporating chromatic noise components. Psychophysical experiments were designed to evaluate color noise subjectively. In the first step, Weber's Law and Fechner's Law were used to generate a standard ruler with equal perceptual interval steps, which served to anchor a numerical image quality rating scale for the subjective rating experiment in the second step. An objective noise evaluation equation that incorporated the noise sensitivity functions modeled from the experimental data was found to better correlate with subjective evaluation. A more robust noise evaluation equation will be derived based on the psychophysical experiment techniques and experimental data in the future.
Reproduction of more pleasing colors is one of the efficient methods to improve image quality of color imaging devices. A psychophysical experiment was completed to investigate the preferred colors for three main categories: human skin, blue sky and green grass. A new experimental technique, the cube-selection method, was developed to adjust lightness, chroma and hue to find out observer’s preference in CIELAB color space. It is a fast and accurate multi-dimensional adjustment technique superior to the conventional method of adjustment. Several potential influence factors for image color preference including image content, capturing illuminant, object background and culture difference were studied by comparing the observers’ preference. Applicable conclusions were drawn from the analysis of the experiment results that help better understand the influence of these factors. It showed that capturing illuminant and image content had significant influence for human skin and grass color reproduction preference respectively. The results from this paper show the way for further research on influence factors of color preference in photographic color reproduction.
KEYWORDS: Interference (communication), RGB color model, Color difference, Color reproduction, Error analysis, Image quality, Cameras, Digital color imaging, Digital imaging, Imaging devices
In digital color imaging, the color information of objects should be reproduced as accurate as possible (unless preferred
color reproduction is demanded), on the other side, the intrinsic imaging noise will propagate to the captured image and
affect the final image quality. Previous studies have not shown how the color accuracy or noise reduction should be
emphasized. Both the noise performance and color accuracy performance should be balanced in order to achieve better
total perceived image quality.
In this paper, a new comprehensive error metric that is a flexible trade-off between color accuracy and RMS noise is
proposed. The linear matrix that converts the device signals to device independent color signals is analytically optimized
by minimizing this comprehensive error metric. By changing the weights to the color and noise components, one can
expect a reproduced image that achieves better color accuracy yet more noise, or an image that has worse color accurate
but less noise, depending on applications and capture conditions. The analytical approach presents a full perspective of
the color and noise characteristics in digital color imaging devices.
The optimal design of spectral sensitivity functions for digital color imaging devices has been studied extensively. This paper analyzed the important requirements for designing sensor sensitivity functions. A hierarchical approach is proposed to the optimal design of camera spectral sensitivity functions by incorporating spectral fitting, colorimetric performance and noise. The approach is directly based on the filter fabrication parameters to avoid approximation deviation. A six-channel camera is designed via this approach, with the first three channels aiming at colorimetric performance and the total six channels for spectral performance.
The spectral characterization of digital imaging devices overcomes the drawbacks of conventional colorimetric characterization by determining the sensor spectral sensitivity functions. Direct measurement of the sensitivities requires expensive instruments and takes long time. A “quick and easy” yet accurate enough estimation of those functions are desired in some circumstances. The estimation is realized by imaging some selected set of reflectance samples. In this paper, some primary available approaches to the sensor sensitivity estimation are reviewed, followed by the description of the proposed iterative multiscale basis functions method. The performance of the new method is compared with some of the available approaches. The implementation of the new method is relatively simple and the results show that it is superior by offering more degrees of freedoms and yielding nonnegative, smooth, and close approximation under either noiseless or noisy condition.
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