Color reproduction using “Black-Point Adaptation”

Based on the current state of CIECAM97s, there is a missing adjustment associated with a black-point unlike a white-point. As an attempt to improve the performance of CIECAM97s for color reproduction, six algorithms focusing on “black-point adaptation” were generated based on previous work on white-point adaptation methods and gamut mapping methods. The six algorithms were used to reproduce four original images targeted to four simulated hard-copy viewing environments that were only differentiated by their black-point settings. Then, the six algorithms were tested in a psychophysical experiment with 32 observers. As a result, linear lightness rescaling under the luminances of white and black of a specific setting was demonstrated to be the best color reproduction method across different black-point settings. The adapted black-point was defined as having the lowest lightness value with its default chromatic appearance correlates predicted by the current state of CIECAM97s under the input viewing environment and was reproduced accordingly with the same appearance correlates

A Black-Point Adaption model for color reproduction

Based on the current state of CIECAM97s, there is a missing adjustment associated with a black-point unlike a white-point. As an attempt to improve the performance of CIECAM97s for color reproduction, six algorithms focusing on black-point adaptation were generated based on previous work on white-point adaptation methods and gamut mapping methods. The six algorithms were used to reproduce four original images targeted to four simulated hard-copy viewing environments that were only differentiated by their black-point settings. Then, the six algorithms were tested in a psychophysical experiment with 32 observers. As a result, linear lightness rescaling under the luminances of white and black of a specific setting was demonstrated to be the best color reproduction method across different black-point settings. The adapted black-point was defined as having the lowest lightness value with its default chromatic appearance correlates predicted by the current state of CIECAM97s under the input viewing environment and was reproduced accordingly with the same appearance correlates

Gamut extension algorithm development and evaluation for the mapping of standard image content to wide-gamut displays

Wide-gamut display technology has provided an excellent opportunity to produce visually pleasing images, more so than in the past. However, through several studies, including Laird and Heynderick, 2008, it was shown that linearly mapping the standard sRGB content to the gamut boundary of a given wide-gamut display may not result in optimal results. Therefore, several algorithms were developed and evaluated for observer preference, including both linear and sigmoidal expansion algorithms, in an effort to define a single, versatile gamut expansion algorithm (GEA) that can be applied to current display technology and produce the most preferable images for observers. The outcome provided preference results from two displays, both of which resulted in large scene dependencies. However, the sigmoidal GEAs (SGEA) were competitive with the linear GEAs (LGEA), and in many cases, resulted in more pleasing reproductions. The SGEAs provide an excellent baseline, in which, with minor improvements, could be key to producing more impressive images on a wide-gamut display

Derivation and modelling hue uniformity and development of the IPT color space

Metric color spaces have been determined to be significantly non-uniform in the hue attribute of color appearance. Several independent sources have confirmed the non-uniformity. A data set was obtained during the course of this thesis work that contains the largest sampling of color space to date which can be used to compare models of color appearance. The data set obtained was compared to existing data sets and found to correspond closely. Lookup table methods were employed to test significant differences between data sets. A simple modeling approach was taken based on commonly understood color space models and knowledge of the visual system. Several color spaces can be derived using the simple model, and one was chosen that models hue uniformity very well and has other desirable attributes. This new color space is named IPT. Many visual data sets were plotted in the IPT color space and all show improved performance over industry standard color spaces. The IPT color space has applications in color data representation, gamut mapping, and color appearance modeling

A Paradigm for color gamut mapping of pictorial images

In this thesis, a paradigm was generated for color gamut mapping of pictorial images. This involved the development and testing of: 1.) a hue-corrected version of the CIELAB color space, 2.) an image-dependent sigmoidal-lightness-rescaling process, 3.) an image-gamut- based chromatic-compression process, and 4.) a gamut-expansion process. This gamut-mapping paradigm was tested against some gamut-mapping strategies published in the literature. Reproductions generated by gamut mapping in a hue-corrected CIELAB color space more accurately preserved the perceived hue of the original scenes compared to reproductions generated using the CIELAB color space. The results of three gamut-mapping experiments showed that the contrast-preserving nature of the sigmoidal-lightness-remapping strategy generated gamut-mapped reproductions that were better matches to the originals than reproductions generated using linear-lightness-compression functions. In addition, chromatic-scaling functions that compressed colors at a higher rate near the gamut surface and less near the achromatic axis produced better matches to the originals than algorithms that performed linear chroma compression throughout color space. A constrained gamut-expansion process, similar to the inverse of the best gamut-compression process found in this experiment, produced reproductions preferred over an expansion process utilizing unconstrained linear expansion

Investigating the Effect of Color Gamut Mapping Quantitatively and Visually

With the advent of various color management standards and tools, the print media industry has seen many advancements aimed towards quantitatively and qualitatively acceptable color reproduction. This research attempts to test one of the most fundamental and integral parts of a standard color management workflow, the profile. The gamut mapping techniques implemented by the ICC profiles created using different profiling application programs were tested for their congruity to the theoretical concepts, standards, and definitions documented by International Color Consortium (ICC). Once these profiling software applications were examined, the significance of the possible discrepancies were tested by establishing a visual assessment of pictorial images using these profiles. In short, this research assessed the implementations of the ICC color rendering intents in a standard or a commonly used color managed workflow, and then described the significance of these discrepancies in terms of interoperability. For this research, interoperability was defined the assessment of different ICC profiles in producing similar results, i.e., quantitatively and visually. In order to achieve the desired assessment, the two profiling applications were selected and each used to create an output profile using the same characterization data set. The two profiles were then compared for differences in the way they mapped real world colors. The results displayed that even though there were some significant quantitative color differences, visual subjective evaluation did not reflect any noticeable color differences and therefore concluded that the profiles were interoperable. These findings reveal that even though quantitative color differences may reflect significant color differences, subjective visual comparisons may not always reflect the same or agree with quantitative findings

The effect of image size on the color appearance of image reproductions

Original and reproduced art are usually viewed under quite different viewing conditions. One of the interesting differences in viewing condition is size difference. The main focus of this research was investigation of the effect of image size on color perception of rendered images. This research had several goals. The first goal was to develop an experimental paradigm for measuring the effect of image size on color appearance. The second goal was to identify the most affected image attributes for changes of image size. The final goal was to design and evaluate algorithms to compensate for the change of visual angle (size). To achieve the first goal, an exploratory experiment was performed using a colorimetrically characterized digital projector and LCD. The projector and LCD were light emitting devices and in this sense were similar soft-copy media. The physical sizes of the reproduced images on the LCD and projector screen could be very different. Additionally, one could benefit from flexibility of soft-copy reproduction devices such as real-time image rendering, which is essential for adjustment experiments. The capability of the experimental paradigm in revealing the change of appearance for a change of visual angle (size) was demonstrated by conducting a paired-comparison experiment. Through contrast matching experiments, achromatic and chromatic contrast and mean luminance of an image were identified as the most affected attributes for changes of image size. Measurement of the extent and trend of changes for each attribute were measured using matching experiments. Proper algorithms to compensate for the image size effect were design and evaluated. The correction algorithms were tested versus traditional colorimetric image rendering using a paired-comparison technique. The paired-comparison results confirmed superiority of the algorithms over the traditional colorimetric image rendering for the size effect compensation

Colour Characterisation of LCD Display Systems

The main purpose of this research is to study the colour characterisation of digital display systems. Three distinct models for characterisation (GOG, PLCC and PLVC) are evaluated and compared and for two of these models (GOG and PLCC) two different sets of linearisation samples (either colour-ramps or grey-ramp samples) are used to perform the linearisation. To evaluate these models’ colorimetric measurements are made for 20 different display devices and colour characterization performance is reported as the main measure. Characterisation performance is calculated using several sets of samples including the widely used Macbeth ColorChecker chart and two new charts called Chart4 and Matlab60 (one of which was based on a method previously published by Cheung and Westland and another was based on a new method). A key aspect of this work is that all 256 levels of intensity were measured for the colour-ramps and for the grey-ramp linearisation samples for each of the 20 displays to allow subsampling of these data to explore the effect of the number of linearisation samples on characterisation performance. When the number of linearisation samples used was small (less than 10) the GOG model sometimes resulted in the smallest characterisation colour differences. However, for the PLCC and PLVC models performance tended to increase with the number of linearization samples and both of these models outperformed GOG with more 10 linearisation samples. For the PLCC model, better performance was usually obtained using the grey-ramp linearisation samples rather than using the colour-ramps linearization samples. It was possible, for each of the 20 displays, to reach average ab values that are less than 1.5 (ab <1.5, 90%) or ab < 1.0 (75%); however, the model that yields the best performance is difficult to ascertain in advance (a good strategy would be to evaluate all five models and select the one that performs best for the characterisation of any particular display). However, in the majority of cases, lowest colour differences (ab) were obtained using the PLCC model and all 256 of the grey-ramp samples for linearisation. This work has compared the performance of five different models using a large number of displays and has allowed a number of recommendations to be made about display characterisation. Although the majority of the work in this thesis was based on stationary displays the effect of motion on characterization performance was also explored. This is important since moving images are now commonplace in many applications. The results showed that a moving background has a small, but statistically significant, effect on the colour of patches

TESTING COLOR APPEARANCE MODELS IN COMPLEX SCENE

The sensation of sight is our primary mechanism to perceive the world around us. However it is not yet perfectly clear how the human visual system works. The images of the world are formed on the retina, captured by sensors and converted in signals sent to the brain. Here the signals are processed and somehow interpreted, thus we are able to see. A lot of information, hypothesis, hints come from a field of the optical (or visual) illusions. These illusions have led many scientists and researchers to ask themselves why we are not able to interpret in a correct way some particular scenes. The word \u201cinterpret\u201d underlines the fact that the brain, and not only the eye, is involved in the process of vision. If our sight worked as a measurement tool, similar to a spectrophotometer, we would not perceive, for example, the simultaneous contrast phenomenon, in which a grey patch placed on a black background appears lighter than an identical coloured patch on a white background. So, why do we perceive the patches as different, while the light that reaches the eyes is the same? In the same way we would not be able to distinguish a white paper seen in a room lit with a red light from a red paper seen under a white light, however humans can do this. These phenomena are called colour appearance phenomena. Simulating the appearance is the objective of a range of computational models called colour appearance models. In this dissertation themes about colour appearance models are addressed. Specific experiments, performed by human observers, aim to evaluate and measure the appearance. Different algorithms are tested in order to compare the results of the computational model with the human sensations about colours. From these data, a new printing pipeline is developed, able to simulate the appearance of advertising billboard in different context

Evaluation and optimal design of spectral sensitivities for digital color imaging

The quality of an image captured by color imaging system primarily depends on three factors: sensor spectral sensitivity, illumination and scene. While illumination is very important to be known, the sensitivity characteristics is critical to the success of imaging applications, and is necessary to be optimally designed under practical constraints. The ultimate image quality is judged subjectively by human visual system. This dissertation addresses the evaluation and optimal design of spectral sensitivity functions for digital color imaging devices. Color imaging fundamentals and device characterization are discussed in the first place. For the evaluation of spectral sensitivity functions, this dissertation concentrates on the consideration of imaging noise characteristics. Both signal-independent and signal-dependent noises form an imaging noise model and noises will be propagated while signal is processed. A new colorimetric quality metric, unified measure of goodness (UMG), which addresses color accuracy and noise performance simultaneously, is introduced and compared with other available quality metrics. Through comparison, UMG is designated as a primary evaluation metric. On the optimal design of spectral sensitivity functions, three generic approaches, optimization through enumeration evaluation, optimization of parameterized functions, and optimization of additional channel, are analyzed in the case of the filter fabrication process is unknown. Otherwise a hierarchical design approach is introduced, which emphasizes the use of the primary metric but the initial optimization results are refined through the application of multiple secondary metrics. Finally the validity of UMG as a primary metric and the hierarchical approach are experimentally tested and verified