The contrast response function of early visual evoked potentials elicited by sinusoidal gratings is known to exhibit characteristic potentials closely associated with the processes of parvocellular and magnocellular pathways. Specifically, the N1 component has been linked with parvocellular processes, while the P1 component has been linked with magnocellular processes. However, little is known regarding the response properties of the N1 and P1 components during the processing and encoding of complex (i.e., broadband) stimuli such as natural scenes. Here, we examine how established physical characteristics of natural scene imagery modulate the N1 and P1 components in humans by providing a systematic investigation of component modulation as visual stimuli are gradually built up from simple sinusoidal gratings to highly complex natural scene imagery. The results suggest that the relative dominance in signal output of the N1 and P1 components is dependent on spatial frequency (SF) luminance contrast for simple stimuli up to natural scene imagery possessing few edges. However, such a dependency shifts to a dominant N1 signal for natural scenes possessing abundant edge content and operates independently of SF luminance contrast.

Efficient coding has been proposed to play an essential role in early visual processing. While several approaches used an objective function to optimize a particular aspect of efficient coding, such as the minimization of mutual information or the maximization of sparseness, we here explore how different estimates of efficient coding in a model with nonlinear dynamics and Hebbian learning determine the similarity of model receptive fields to V1 data with respect to spatial tuning. Our simulation results indicate that most measures of efficient coding correlate with the similarity of model receptive field data to V1 data, that is, optimizing the estimate of efficient coding increases the similarity of the model data to experimental data. However, the degree of the correlation varies with the different estimates of efficient coding, and in particular, the variance in the firing pattern of each cell does not predict a similarity of model and experimental data.

Observers can generally make reliable judgments of surface color in natural scenes despite changes in an illuminant that is out of view. This ability has sometimes been attributed to observers' estimating the spectral properties of the illuminant in order to compensate for its effects. To test this hypothesis, two surface-color-matching experiments were performed with images of natural scenes obtained from high-resolution hyperspectral images. In the first experiment, the sky illuminating the scene was directly visible to the observer, and its color was manipulated. In the second experiment, a large gray sphere was introduced into the scene so that its illumination by the sun and sky was also directly visible to the observer, and the color of that illumination was manipulated. Although the degree of color constancy varied across this and other variations of the images, there was no reliable effect of illuminant color. Even when the sky was eliminated from view, color constancy did not worsen. Judging surface color in natural scenes seems to be independent of an explicit illuminant cue.

To what extent do observers' judgments of surface color with natural scenes depend on global image statistics? To address this question, a psychophysical experiment was performed in which images of natural scenes under two successive daylights were presented on a computer-controlled high-resolution color monitor. Observers reported whether there was a change in reflectance of a test surface in the scene. The scenes were obtained with a hyperspectral imaging system and included variously trees, shrubs, grasses, ferns, flowers, rocks, and buildings. Discrimination performance, quantified on a scale of 0 to 1 with a color-constancy index, varied from 0.69 to 0.97 over 21 scenes and two illuminant changes, from a correlated color temperature of 25,000 K to 6700 K and from 4000 K to 6700 K. The best account of these effects was provided by receptor-based rather than colorimetric properties of the images. Thus, in a linear regression, 43% of the variance in constancy index was explained by the log of the mean relative deviation in spatial cone-excitation ratios evaluated globally across the two images of a scene. A further 20% was explained by including the mean chroma of the first image and its difference from that of the second image and a further 7% by the mean difference in hue. Together, all four global color properties accounted for 70% of the variance and provided a good fit to the effects of scene and of illuminant change on color constancy, and, additionally, of changing test-surface position. By contrast, a spatial-frequency analysis of the images showed that the gradient of the luminance amplitude spectrum accounted for only 5% of the variance.

Simple color-difference formulae and pictorial images have traditionally been used to estimate the visual impact of color errors introduced by image-reproduction processes. But the limited gamut of RGB cameras constrains such analyses, particularly of natural scenes. The purpose of this work was to estimate visual sensitivity to color errors introduced deliberately into pictures synthesized from hyperspectral images of natural scenes without gamut constraints and to compare discrimination thresholds expressed in CIELAB and S-CIELAB color spaces. From each original image, a set of approximate images with variable color errors were generated and displayed on a calibrated RGB color monitor. The threshold for perceptibility of the errors was determined in a paired-comparison experiment. In agreement with previous studies, it was found that discrimination between original and approximate images needed on average a CIELAB color difference ΔEab* of about 2.2. Although a large variation of performance across the nine images tested was found when errors were expressed in CIELAB units, little variation was obtained when they were expressed in S-CIELAB units.

Deuteranomalous trichromacy, which affects medium-wavelength-sensitive cones, is more common than protanomalous trichromacy, which affects long-wavelength-sensitive cones. The aim of the present work was to test the extent to which these two kinds of anomalous trichromacy affect surface-color judgments in the natural world. Simulations of 18 natural scenes under different daylight illuminants were presented on a high-resolution color monitor to 7 deuteranomalous, 7 protanomalous, and 12 normal trichromatic observers, who had to discriminate between reflectance and illuminant changes in the images. Observers' ability to judge surface color was quantified by a standard color-constancy index. Deuteranomalous trichromats performed as well as normal trichromats, but protanomalous trichromats performed more poorly than both. The results are considered in relation to the spectral coverage of cones, rod intrusion, and the characterization of anomalous trichromacy by the Rayleigh match.

If surfaces in a scene are to be distinguished by their color, their neural representation at some level should ideally vary little with the color of the illumination. Four possible neural codes were considered: von-Kries-scaled cone responses from single points in a scene, spatial ratios of cone responses produced by light reflected from pairs of points, and these quantities obtained with sharpened (opponent-cone) responses. The effectiveness of these codes in identifying surfaces was quantified by information-theoretic measures. Data were drawn from a sample of 25 rural and urban scenes imaged with a hyperspectral camera, which provided estimates of surface reflectance at 10-nm intervals at each of 1344 × 1024 pixels for each scene. In computer simulations, scenes were illuminated separately by daylights of correlated color temperatures 4000 K, 6500 K, and 25,000 K. Points were sampled randomly in each scene and identified according to each of the codes. It was found that the maximum information preserved under illuminant changes varied with the code, but for a particular code it was remarkably stable across the different scenes. The standard deviation over the 25 scenes was, on average, approximately 1 bit, suggesting that the neural coding of surface color can be optimized independent of location for any particular range of illuminants.