Influence of color spaces over texture characterization

Images are generally represented in the RGB color space. This is the model commonly used for most cameras and for displaying on computer screens. Nevertheless, the representation of color images using this color space has some important drawbacks for image analysis. For example, it is a non-uniform space, that is, measured color differences are not proportional to the human perception of such differences. On the other hand, HSI color space is closer to the human color perception and CIE Lab color space has been defined to be approximately uniform. In this work, the influence of the color space for color texture characterization is studied by comparing Lab, HSI, and RGB color spaces. Their effectiveness is analyzed regarding their influence over two different texture characterization methods: DFT features and co-occurrence matrices. The results have shown that involving color information into texture analysis improves the characterization significantly. Moreover, Lab and HSI color spaces outperform RG

Fractal Dimensions in Perceptual Color Space: A Comparison Study Using Jackson Pollock's Art

The fractal dimensions of color-specific paint patterns in various Jackson Pollock paintings are calculated using a filtering process which models perceptual response to color differences (\Lab color space). The advantage of the \Lab space filtering method over traditional RGB spaces is that the former is a perceptually-uniform (metric) space, leading to a more consistent definition of ``perceptually different'' colors. It is determined that the RGB filtering method underestimates the perceived fractal dimension of lighter colored patterns but not of darker ones, if the same selection criteria is applied to each. Implications of the findings to Fechner's 'Principle of the Aesthetic Middle' and Berlyne's work on perception of complexity are discussed.Comment: 21 pp LaTeX; two postscript figure

Uniform color spaces based on CIECAM02 and IPT color difference equations

Color difference equations based on the CIECAM02 color appearance model and IPT color space have been developed to fit experimental data. There is no color space in which these color difference equations are Euclidean, e.g. describe distances along a straight line. In this thesis, Euclidean color spaces have been derived for the CIECAM02 and IPT color difference equations, respectively, so that the color difference can be calculated as a simple color distance. Firstly, the Euclidean line element was established, from which terms were derived for the new coordinates of lightness, chroma, and hue angle. Then the spaces were analyzed using performance factors and statistics to test how well they fit various data. The results show that the CIECAM02 Euclidean color space has performance factors similar to the optimized CIECAM02 color difference equation. To statistical significance, the CIECAM02 Euclidean color space had superior fit to the data when compared to the CIECAM02 color difference equation. Conversely, the IPT Euclidean color space performed poorer than the optimized IPT color difference equation. The main reason is that the line element for the lightness vector dimension could not be directly calculated so an approximation was used. To resolve this problem, a new IPT color difference equation should be designed such that line elements can be established directly

Digital halftoning using fibonacci-like sequence pertubation and using vision-models in different color spaces

A disadvantage in error diffusion is that it creates objectionable texture patterns at certain gray levels. An approach, threshold perturbation by Fibonacci-like sequences, was studied. This process is simpler than applying a vision model and it also decreases the visible patterns in error diffusion. Vector error diffusion guarantees minimum color distance in binarization provided that a uniform color space is used. Four color spaces were studied in this research. It was found that vector error diffusion in two linear color spaces made no reduction in the quality of halftoning compared with that in CIEL*a*b* or CIEL*u*v* color spaces. A luminance vision MTF and a chroma vision MTF were used in model-based error diffusion to further improve the halftone image quality

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

Circular Average Filtering and Circular Linear Interpolation in Complex Color Spaces

In color spaces where the chromatic term is given in polar coordinates, the shortest distance between colors of the same value is circular. By converting such a space into a complex polar form with a real-valued value axis, a color algebra for combining colors is immediately available. In this work, we introduce two complex space operations utilizing this observation: circular average filtering and circular linear interpolation. These operations produce Archimedean Spirals, thus guaranteeing that they operate along the shortest paths. We demonstrate that these operations provide an intuitive way to work in certain color spaces and that they are particularly useful for obtaining better filtering and interpolation results. We present a set of examples based on the perceptually uniform color space CIELAB or L*a*b* with its polar form CIEHLC. We conclude that representing colors in a complex space with circular operations can provide better visual results by exploitation of the strong algebraic properties of complex space C.Comment: 10 page

Transformacije i povezanost različitih sustava za definiranje boje

Analiziraju se počeci objektivnog definiranja boja i njihova pozicija u odreĎenom prostoru boja. Razmatraju se osnovni CIE standardi i CIE prostori boja. Proučava se evolucija CIE prostora boja u dvadesetom stoljeću prema uniformnim prostorima boja. Proučavaju se prostori boja, kao i transformacije vrijednosti jednog prostora boja u drugi.The beginnings of objective color definition and their position in a given color space are analyzed. Basic CIE standards and CIE color spaces are considered. The evolution of CIE color space in the twentieth century is studied in uniform color spaces. Color spaces, as well as the transformation of the value of a color space into the other, are studied

Color matching functions for a perceptually uniform RGB space

We present methods to estimate perceptual uniformity of color spaces and to derive a perceptually uniform RGB space using geometrical criteria defined in a logarithmic opponent color representation