Zofia Baranczuk

Web-based psychometric evaluation of image quality

The measurement of image quality requires the judgement by the human visual system. This paper describes a psycho-visual test technique that uses the internet as a test platform to identify image quality in a more time-effective manner, comparing the visual response data with the results from the same test in a lab-based environment and estimate the usefulness of the internet as a platform for scaling studies.

Conjoint Analysis for Evaluating Parameterized Gamut Mapping Algorithms

We show that conjoint analysis, a popular multi-attribute preference assessment technique used in market research, is a well suited tool to evaluate a multitude of gamut mapping algorithms simultaneously. Our analysis is based on data from psycho-visual tests assessed in a laboratory and in a web environment. Conjoint analysis allows us to quantify the contribution of every single parameter value to the perceived value of the algorithm; it also allows us to test the influence of additional parameters like gamut size or color shifts. We show that conjoint analysis can be individualized to images or observers if enough data is available. Especially promising in this respect is the combination of individual and population data.

Filter methods to preserve local contrast and to avoid artifacts in gamut mapping

Contrary to high dynamic range imaging, the preservation of details and the avoidance of artifacts is not explicitly considered in popular color management systems. An effective way to overcome these difficulties is image filtering. In this paper we investigate several image filter concepts for detail preservation as part of a practical gamut mapping strategy. In particular we define four concepts including various image filters and check their performance with a psycho-visual test. Additionally, we compare our performance evaluation to two image quality measures with emphasis on local contrast. Surprisingly, the most simple filter concept performs highly efficient and achieves an image quality which is comparable to the more established but slower methods.

Chapter 1 - Gamut Mapping

This chapter provides an overview of research on image-dependent gamut mapping. A color gamut includes all colors that can be rendered by a device or are contained in an image. Mapping the colors from one color gamut into another one is called gamut mapping. To understand the objectives and problems of gamut mapping several aspects need to be covered, namely, the basics of color science, typical image reproduction workflows, psychovisual testing and scaling, and image-quality measures. Data from psychovisual tests and image quality measures allow image-dependent gamut mapping to be considered as an optimization problem, namely, finding the best mapping either as perceived by users or in terms of image quality. We show that many popular gamut-mapping algorithms can be cast in the language of an abstract optimization framework and discuss the aspects optimized by the different algorithms.