Visual sensitivity, blur and the sources of variability in the amplitude spectra of natural scenes

Abstract

A number of researchers have suggested that in order to understand the response properties of cells in the visual pathway, we must consider the statistical structure of the natural environment. In this paper, we focus on one aspect of that structure, namely, the correlational structure which is described by the amplitude or power spectra of natural scenes. We propose that the principle insight one gains from considering the image spectra is in understanding the relative sensitivity of cells tuned to different spatial frequencies. This study employs a model in which the peak sensitivity is constant as a function of frequency with linear bandwith increasing (i.e., approximately constant in octaves). In such a model, the "response magnitude" (i.e., vector length) of cells increases as a function of their optimal (or central) spatial frequency out to about 20 cyc/deg. The result is a code in which the response to natural scenes, whose amplitude spectra typically fall as 1/f, is roughly constant out to 20 cyc/deg. An important consideration in evaluating this model of sensitivity is the fact that natural scenes show considerable variability in their amplitude spectra, with individual scenes showing falloffs which are often steeper or shallower than 1/f. Using a new measure of image structure (the "rectified contrast spectrum" or "RCS") on a set of calibrated natural images, it is shown that a large part of the variability in the spectra is due to differences in the sparseness of local structure at different scales. That is, an image which is "in focus" will have structure (e.g., edges) which has roughly the same magnitude across scale. That is, the loss of high frequency energy in some images is due to the reduction of the number of regions that contain structure rather than the amplitude of that structure. An "in focus" image will have structure (e.g., edges) across scale that have roughly equal magnitude but may vary in the area covered by structure. The slope of the RCS was found to provide a reasonable prediction of physical blur across a variety of scenes in spite of the variability in their amplitude spectra. It was also found to produce a good prediction of perceived blur as judged by human subjects.

Subordinate-level categorization relies on high spatial frequencies to a greater degree than basic-level categorization

“…This is because structural information at the object level becomes finer in spatial terms as the object moves farther away, gradually slipping below the acuity limit of the observer. Also, in natural images, low spatial frequencies have much higher contrast levels, with the energy dropping off as a 1/f n function, where f is frequency and n is an exponent between about 1.1 and 2.2 (see, e.g., Field & Brady, 1997;Rainville & Kingdom, 1999). This means that obscuration that reduces contrast-such as glare or fog-will tend to affect the higher frequencies more.…”

Spatial frequency thresholds for face recognition when comparison faces are filtered and unfiltered

“…Boutet et al (2003) suggested, instead, that the middleband advantage might be due to the middle range of frequencies having sufficient amounts of both contrast and spatial information to be useful. The contrast in a natural image drops as 1/f n , where f is frequency and n is an exponent in the range of about 1-2 (Field & Brady, 1997). This means that the amount of contrast in a natural image is halved for every doubling of frequency.…”

A New Method for Evaluating the Visual Quality of Loblolly Pine (Pinus Taeda L.) Forest

“…The limitations of this research can be a subjective judgment of photographs, limited number of photographs examined, and some data accuracy in pixel counting. There have been also several other studies applied fractal dimension such as evaluating the texture discrimination (Keller et al, 1989) and observing luminance patterns in natural scenes (Field and Brady, 1997). We therefore suggest that these techniques may be useful for future studies of investigating visual quality assessment of understory vegetation cover by applying box-counting method and FD which may also elucidate spatial data acquisition techniques in the future.…”