Image appearance modeling

Traditional color appearance modeling has recently matured to the point that available, internationally-recommended models such as CIECAM02 are capable of making a wide range of predictions to within the observer variability in color matching and color scaling of stimuli in somewhat simplified viewing conditions. It is proposed that the next significant advances in the field of color appearance modeling will not come from evolutionary revisions of these models. Instead, a more revolutionary approach will be required to make appearance predictions for more complex stimuli in a wider array of viewing conditions. Such an approach can be considered image appearance modeling since it extends the concepts of color appearance modeling to stimuli and viewing environments that are spatially and temporally at the level of complexity of real natural and man-made scenes. This paper reviews the concepts of image appearance modeling, presents iCAM as one example of such a model, and provides a number of examples of the use of iCAM in still and moving image reproduction

Spectral Image Segmentation with Global Appearance Modeling

We introduce a new spectral method for image segmentation that incorporates long range relationships for global appearance modeling. The approach combines two different graphs, one is a sparse graph that captures spatial relationships between nearby pixels and another is a dense graph that captures pairwise similarity between all pairs of pixels. We extend the spectral method for Normalized Cuts to this setting by combining the transition matrices of Markov chains associated with each graph. We also derive an efficient method that uses importance sampling for sparsifying the dense graph of appearance relationships. This leads to a practical algorithm for segmenting high-resolution images. The resulting method can segment challenging images without any filtering or pre-processing

Color-appearance modeling for cross-media image reproduction

Five color-appearance transforms were tested under a variety of conditions to determine which is best for producing CRT reproductions of original printed images. The transforms included: von Kries chromatic adaptation, CIELAB color space, RLAB color appearance model, Hunt\u27s color appearance model, and Nayatani\u27s color appearance model. It was found that RLAB produced the best matches for changes in white point, luminance level, and background changes, but did not accurately predict the effect of surround. The ability of CIELAB color space was equal to that of RLAB in many cases, and performed better for changes in surround. Expert observers generated CRT images in one viewing condition that they perceived to match an original image viewed in another condition. This technique produced images that were equal to or better than the best color appearance model tested and is a useful technique to generate color appearance data for developing new models and testing existing models

Edge-Aware Image Color Appearance and Difference Modeling

The perception of color is one of the most important aspects of human vision. From an evolutionary perspective, the accurate perception of color is crucial to distinguishing friend from foe, and food from fatal poison. As a result, humans have developed a keen sense of color and are able to detect subtle differences in appearance, while also robustly identifying colors across illumination and viewing conditions. In this paper, we shall briefly review methods for adapting traditional color appearance and difference models to complex image stimuli, and propose mechanisms to improve their performance. In particular, we find that applying contrast sensitivity functions and local adaptation rules in an edge-aware manner improves image difference predictions

THE EFFECT OF SOCIAL COMPARISON ON FEMALE MODELS BODY IMAGE

Being female models are identical to have an attractive physical appearance such as a tall, thin and clean white skin, which this makes the models focus on their physical appearance. Furthermore, models have a tendency to compare themselves with others in order to fulfill the demands of the modeling industry. This study aims to determine whether there is an effect of social comparison on body image in the female model. The sample of this research is 275 female models with an age range of 18-30 years who are in Jakarta and are members of a modeling agency. Data were collected using social comparison and body image measurement tools. Social comparison was measured using Physical Appearance Comparison Scale-Revised (PACS-R) and body image measured using the Multidimensional Body-Self Relations Questionnaire–Appearance Scales (MBSRQ–AS). The results of this study prove that social comparison has a significant effect on the body image of female models in Jakarta. The effective contribution of social comparison to body image is 46.3%. The Implications of the research could be the baseline for women's empowerment institutions in increasing knowledge for women so that they are able to accept their physical appearance as it is. Specifically, this research is actually a reflection for all model agencies in terms of represent beauty to people through products or brand