Spatial frequency, phase, and the contrast of natural images

Abstract

We examined contrast sensitivity and suprathreshold apparent contrast with natural images. The spatial-frequency components within single octaves of the images were removed (notch filtered), their phases were randomized, or the polarity of the images was inverted. Of Michelson contrast, root-mean-square (RMS) contrast, and band-limited contrast, RMS contrast was the best index of detectability. Negative images had lower apparent contrast than their positives. Contrast detection thresholds showed spatial-frequency-dependent elevation following both notch filtering and phase randomization. The peak of the spatial-frequency tuning function was approximately 0.5-2 cycles per degree (c/deg). Suprathreshold contrast matching functions also showed spatial-frequency-dependent contrast loss for both notch-filtered and phase-randomized images. The peak of the spatial-frequency tuning function was approximately 1-3 c/deg. There was no detectable difference between the effects of phase randomization and notch filtering on contrast sensitivity. We argue that these observations are consistent with changes in the activity within spatial-frequency channels caused by the higher-order phase structure of natural images that is responsible for the presence of edges and specularities.

The importance of information localization in scene gist recognition.

“…Previous research has shown that phase randomization reduces contrast (Bex & Makous, 2002;Wichmann et al, 2006), which might explain the decreased masking we found as a function of phase randomization. To explore this possibility, we carried out a control experiment in which we reduced the contrast of each normal image mask to match the perceived contrast of its fully phase-randomized version (using the lower of two raters' perceived contrast ratings), and we compared the gist masking produced by each (reducedcontrast normal image [i.e., RAND ϭ 0] masks and fully phaserandomized image [RAND ϭ 1.0] masks).…”

To Be Seen or to Hide: Visual Characteristics of Body Patterns for Camouflage and Communication in the Australian Giant CuttlefishSepia apama

“…The consistent relationship between amplitude and spatial frequency is well documented, with the power spectrum of a given scene falling roughly as the inverse of the square of the spatial frequency (Shapley and Lennie 1985;Bex and Makous 2002;Balboa and Grzywacz 2003). In other words, the amount of visual information that might be classified as fine scale or coarse scale is very similar between scenes regardless of the habitat type and viewing distance, a phenomenon that is probably due to a fractal-like self-simi- Fourier transform (FT) reveals the spatial distribution encoded in a two-dimensional image, and the resulting power spectra enable us to visualize this information.…”

“…We used 100 frequency bins and ignored the lowest 2% of frequencies to avoid spiking in this region through image artifacts (Kovesi 2000). Because the power contained in an image may span several orders of magnitude, the slopes of log-log power-spatial frequency plots were used to compare the spectra of animal and background in each image, as commonly used in natural scene comparison (Field 1987;Ruderman 1994;Bex and Makous 2002). Linear regression was applied to ascertain how well these data fitted a linear model, and slopes and intercepts obtained from regressions were used to test for differences between the data.…”

Cephalopod dynamic camouflage: bridging the continuum between background matching and disruptive coloration

“…This energy measure is closely related to the root mean square (r.m.s.) contrast typically used in characterizing the contrast of complex scenes (Bex & Makous 2002); specifically, the square root of the sum of the granularity spectrum values would closely approximate the r.m.s. energy in the image (a small amount of energy in the lowest and highest spatial frequencies is discarded).…”