Color constancy is the perceived stability of the color of objects despite changes in the light illuminating them. An object's color is considered constant if the current perceived color is judged to be in accord with the remembered one. Thus the accuracy and precision of color memory are fundamental to understanding this classic problem. Two hypotheses of color memory are tested here: (1) the photoreceptor hypothesis, which states that the color recalled from memory reproduces the light absorbed by each type of cone and (2) the surface-reflectance hypothesis, which states that the color recalled from memory is based on an inferred spectral reflectance of a surface that does not depend on the spectral distribution of the illuminant. In the experiments a test color is surrounded by either (i) a complex pattern composed of several colored patches or (ii) a uniform "gray" field at the chromaticity of the illuminant. In a control condition the test color is presented on a dark background. Long-term memory of the test color is measured in a production task begun 10 min after the end of the learning phase. In general, the results with a complex surround are consistent with the surface-reflectance hypothesis but not with the photoreceptor hypothesis. Color memory with the "gray" surround, on the other hand, shows a much stronger effect of the illuminant used during learning. These results are consistent with computational models of color constancy that require three or more chromaticities in view.

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A softcopy quality ruler method was implemented for the International Imaging Industry Association (I3A) Camera Phone Image Quality (CPIQ) Initiative. This work extends ISO 20462 Part 3 by virtue of creating reference digital images of known subjective image quality, complimenting the hardcopy Standard Reference Stimuli (SRS). The softcopy ruler method was developed using images from a Canon EOS 1Ds Mark II D-SLR digital still camera (DSC) and a point-and-shoot Kodak EasyShare P880 zoom digital camera. Images were viewed on an Apple Cinema HD Display (30" flat panel) at a viewing distance of 34 inches. Ruler images were made for 16 scenes. Thirty ruler images were generated for each scene, representing ISO 20462 Standard Quality Scale (SQS) values of approximately 2 to 31 at an increment of one just noticeable difference (JND) by adjusting the system modulation transfer function (MTF). A Matlab GUI was developed to display the ruler and test images side-by-side with a user-adjustable ruler level controlled by a slider. Two validation studies were performed at Eastman Kodak Company, Vista Point Technology, and Aptina Imaging in which all three companies set up a similar viewing lab to run the softcopy ruler method. The results show that the three sets of data are in reasonable agreement with each other, with the differences within the range expected from observer variability. Compared to previous implementations of the quality ruler, the slider-based user interface allows approximately 2x faster assessments with 20% better precision.

60439. For information about Argonne and its pioneering science and technology programs, see www.anl.gov.

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Texture appearance is an important component of photographic image quality as well as object recognition. Noise cleaning algorithms are used to decrease sensor noise of digital images, but can hinder texture elements in the process. The Camera Phone Image Quality (CPIQ) initiative of the International Imaging Industry Association (I3A) is developing metrics to quantify texture appearance. Objective and subjective experimental results of the texture metric development are presented in this paper. Eight levels of noise cleaning were applied to ten photographic scenes that included texture elements such as faces, landscapes, architecture, and foliage. Four companies (Aptina Imaging, LLC, Hewlett-Packard, Eastman Kodak Company, and Vista Point Technologies) have performed psychophysical evaluations of overall image quality using one of two methods of evaluation. Both methods presented paired comparisons of images on thin film transistor liquid crystal displays (TFT-LCD), but the display pixel pitch and viewing distance differed. CPIQ has also been developing objective texture metrics and targets that were used to analyze the same eight levels of noise cleaning. The correlation of the subjective and objective test results indicates that texture perception can be modeled with an objective metric. The two methods of psychophysical evaluation exhibited high correlation despite the differences in methodology.