Sn Sergej Yendrikhovskij

Color reproduction and the naturalness constraint

This article aims to determine the process underlying the subjective impression about the fidelity of reproduced object colors. To this end, we present the concept of the naturalness constraint and a framework for specification of naturalness judgments. We consider several research questions that are essential for this framework and discuss plausible answers supported by experiments. In general, naturalness assessment of reproduced object colors can be (1) defined as similarity to prototypical object colors, and (2) characterized by a probability density function (e.g., Gaussian). Experiments show that (3) there is a considerable amount of consistency in naturalness judgments of locally and globally processed images (although observers are slightly more tolerant of global image processing), and (4) naturalness judgments vary for different object categories; e.g., subjects are more consistent in naturalness judgments of skin, grass, and sky reproductions than shirt reproduction. We suggest that (5) naturalness of a whole picture is determined by the naturalness of the most critical object in that picture. Finally, we introduce a naturalness index predicting perceived naturalness of color reproduction of real-life scenes.

Representation of memory prototype for an object color

This article presents a general framework for modeling memory colors and provides experimental evi- dence supporting this model for one particular object, i.e., a banana. We propose to characterize memory colors from experimentally determined similarity judgments of apparent object colors with the prototypical color of that object category. The aim of the first experiment was to analyze the memory representation of banana color in the CIELUV color space. To this end, we prepared images imitating different colors of a banana and asked subjects to scale the similarity in color of a banana shown on a CRT display and a typical ripe banana as it is remembered from their past experience. The relationships between the similarity judg- ments and chromaticity coordinates representing the ma- nipulated banana samples can be well described by a biva- riate normal distribution. Another experiment was carried out to gain more insight into the perception process leading to an appearance of the banana stimuli. Additionally, a sample of banana colors from a fruit market was measured and compared with the similarity judgments.© 1999 John Wiley & Sons, Inc. Col Res Appl, 24, 393- 410, 1999

A computational model of colour categorization

The following research elaborates on understanding and modeling the colour categorization process. The structure of colour categories is argued to resemble the structure of the distribution of colours in the perceived world. This distribution can be represented as colour statistics in some perceptual and approximately uniform colour space (e.g., the CIELUV colour space). The process of colour categorization can be modeled through the grouping of colour statistics by clustering algorithms (e.g., K-means) based on the minimum-distance criterion. This model explains the location and emergence of colour categories. The number of colour categories is presumably determined by a trade-off between accuracy in representation of the perceived world and simplicity of the category system. The model is examined on the basis of K-means clustering analysis of statistics of 630 natural images in the CIELUV colour space.

Colourfulness judgments of natural scenes

Abstract The purpose of this study was to examine the relationship between colourfulness judgments of images of natural scenes and statistical parameters of the chroma distribution over the images in the CIE 1976 L∗u∗v∗ (CIELUV) colour space. A comparative analysis of within-scenes and between-scenes colourfulness assessments was performed. Images were created by varying chroma in the CIELUV colour space while lightness and hue-angle were kept constant. Experiment 1 shows the results of a multidimensional analysis of the differences-scaling in perceived colourfulness. Experiment 2 includes a unidimensional investigation of the direct-scaling of the magnitude of perceived colourfulness. Both experiments confirm that colourfulness judgments highly correlate with a linear combination of the mean of the distribution of chroma values and its standard deviation. That was also verified in Experiment 3 with “Mondrian”-like stimuli (spatially scrambled versions of the images of natural scenes). Colourfulness judgments of natural scenes, but not “Mondrian”-like scenes, brought out systematic differences between observers. In order to interpret and quantify these differences, a model of individual strategies in colourfulness judgments is developed and discussed.