Predicting Image Differences Based on Image-Difference Features
Abstract:An accurate image-difference measure would greatly simplify the optimization of imaging systems and image processing algorithms. The prediction performance of existing methods is limited because the visual mechanisms responsible for assessing image differences are not well understood. This applies especially to the cortical processing of complex visual stimuli.
We propose a flexible image-difference framework that models these mechanisms using an empirical data-mining strategy. A pair of input images is first normalized to specific viewing conditions by an image appearance model. Various image-difference features (IDFs) are then extracted from the images. These features represent assumptions about visual mechanisms that are responsible for judging image differences. Several IDFs are combined in a blending step to optimize the correlation between image-difference predictions and corresponding human assessments.
We tested our method on the Tampere Image Database 2008, where it showed good correlation with subjective judgments. Comparisons with other image-difference measures were also performed.
Document Type: Research Article
Publication date: January 1, 2011
CIC is the premier annual technical gathering for scientists, technologists, and engineers working in the areas of color science and systems, and their application to color imaging. Participants represent disciplines ranging from psychophysics, optical physics, image processing, color science to graphic arts, systems engineering, and hardware and software development. While a broad mix of professional interests is the hallmark of these conferences, the focus is color. CICs traditionally offer two days of short courses followed by three days of technical sessions that include three keynotes, an evening lecture, and a vibrant interactive papers session. An endearing symbol of the meeting is the Cactus Award, given each year to the author(s) of the best interactive paper presentation.
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