Perceived Depth in Natural Images Reflects Encoding of Low-Level Luminance Statistics

Abstract

Sighted animals must survive in an environment that is diverse yet highly structured. Neural-coding models predict that the visual system should allocate its computational resources to exploit regularities in the environment, and that this allocation should facilitate perceptual judgments. Here we use three approaches (natural scenes statistical analysis, a reanalysis of single-unit data from alert behaving macaque, and a behavioral experiment in humans) to address the question of how the visual system maximizes behavioral success by taking advantage of low-level regularities in the environment. An analysis of natural scene statistics reveals that the probability distributions for light increments and decrements are biased in a way that could be exploited by the visual system to estimate depth from relative luminance. A reanalysis of neurophysiology data from Samonds et al. (2012) shows that the previously reported joint tuning of V1 cells for relative luminance and binocular disparity is well matched to a predicted distribution of binocular disparities produced by natural scenes. Finally, we show that a percept of added depth can be elicited in images by exaggerating the correlation between luminance and depth. Together, the results from these three approaches provide further evidence that the visual system allocates its processing resources in a way that is driven by the statistics of the natural environment.

Predicting Cortical Dark/Bright Asymmetries from Natural Image Statistics and Early Visual Transforms

“…Confirming and expanding on previous results, we found that bright and dark visual features are distributed asymmetrically in terms of their contrast levels and relative depths [ 12 , 15 , 26 ]. In addition, we found that the spatial frequency content of natural scenes differs substantially between brights and darks, with a higher dark bias at low spatial frequencies.…”

“…This dark/bright dichotomy, however, has been largely overlooked in the study of natural scene statistics. There are three relevant observations that motivate our analysis: natural scenes contain more dark visual contrast [ 20 , 24 , 25 ], this dark bias increases at higher contrast levels [ 15 ], and dark visual contrasts also tend to be associated with farther relative depths [ 12 , 19 , 26 ]. These observations led us to hypothesize that the bright and dark visual features of natural images may differ along other dimensions as well.…”

Sensory uncertainty leads to systematic misperception of the direction of motion in depth

“…This pattern of results is consistent with a Bbrighter is closer^heuristic. This heuristic mirrors the relationship between contrast and depth in natural scenes, sometimes known as proximity-luminance covariance (Coules, 1955;Schwartz & Sperling, 1983;Dosher, Sperling, & Wurst, 1986) and recently has been shown to bias observer reports of perceived depth in natural images (Cooper & Norcia, 2014). Thus, in the context of our study, observers may be influenced by a prior expectation of target position in addition to any prior expectations of target motion, especially in cases where sensory uncertainty is already high.…”

Signs of depth-luminance covariance in 3-D cluttered scenes

“…That is, cells with near or far disparities tend to be sensitive to local luminance maxima and minima respectively (Samonds et al, 2012). These effects have been reanalyzed and confirmed by Cooper and Norcia (2014) who also reported on a psychophysical study. Using natural images along with registered depth maps, they showed the perception of 3-D depth could be enhanced or diminished by biasing the image intensities toward a negative or positive depth-luminance covariance, respectively.…”