In order to predict the overall perception of image quality it is necessary to first understand and quantify the appearance of images. Just as color appearance modeling evolved from traditional colorimetry and color difference calculations, image appearance modeling evolves from color image difference calculations. A modular framework for the creation of a color image difference metric has been developed and tested using several psychophysical datasets. This framework is based upon traditional CIE color difference equations, and the S-CIELAB spatial extension to the CIELAB color space. The color image difference predictions have been shown to correlate well with experimental data. The color image difference framework was extended predict the overall appearance of images by replacing the CIELAB color space at the heart of the calculations with a color appearance space. An image appearance model maps the physics of complex image stimuli into human perceptions such as lightness, chroma, hue, contrast, sharpness, and graininess. A first generation image appearance model, named iCAM, has been introduced. Via image appearance modeling new techniques for predicting overall image quality, without the need for intimate knowledge of the imaging system design, can be created.
Library of Congress Subject Headings
Imaging systems; Image processing--Digital techniques; Color-printing--Digital techniques; Color computer graphics
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Johnson, Garrett M., "Measuring images: Differences, quality and appearance" (2003). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus