Local masking in natural images: A database and analysis

Abstract

Studies of visual masking have provided a wide range of important insights into the processes involved in visual coding. However, very few of these studies have employed natural scenes as masks. Little is known on how the particular features found in natural scenes affect visual detection thresholds and how the results obtained using unnatural masks relate to the results obtained using natural masks. To address this issue, this paper describes a psychophysical study designed to obtain local contrast detection thresholds for a database of natural images. Via a three-alternative forced-choice experiment, we measured thresholds for detecting 3.7 cycles/8 vertically oriented log-Gabor noise targets placed within an 85 · 85pixels patch (1.98 patch) drawn from 30 natural images from the CSIQ image database (Larson & Chandler, Journal of Electronic Imaging, 2010). Thus, for each image, we obtained a masking map in which each entry in the map denotes the root mean squared contrast threshold for detecting the log-Gabor noise target at the corresponding spatial location in the image. From qualitative observations we found that detection thresholds were affected by several patch properties such as visual complexity, fineness of textures, sharpness, and overall luminance. Our quantitative analysis shows that except for the sharpness measure (correlation coefficient of 0.7), the other tested low-level mask features showed a weak correlation (correlation coefficients less than or equal to 0.52) with the detection thresholds. Furthermore, we evaluated the performance of a computational contrast gain control model that performed fairly well with an average correlation coefficient of 0.79 in predicting the local contrast detection thresholds. We also describe specific choices of parameters for the gain control model. The objective of this database is to provide researchers with a large ground-truth dataset in order to further investigate the properties of the human visual system using natural masks.

Constrained sampling experiments reveal principles of detection in natural scenes

“…Although there have been a number of studies of detection in natural backgrounds (16)(17)(18)(19)(20)(21)(22)(23), they have not directly addressed these questions, and have either tested only a small number of natural stimuli (16,17,19,20), tested natural stimuli with altered statistical properties (21,22), or used experimental paradigms not representative of natural detection tasks (16,(18)(19)(20)23). These latter studies are not as representative of natural tasks, because observers were allowed to directly compare the same image with and without the added target, an advantage that is not normally available under real-world conditions.…”

Masking in chrominance channels of natural images — Data, analysis, and prediction

“…In the second part, we analyze the collected thresholds and their relations with some common statistical image features. This analysis is performed in a similar way as it is in [12]. The data from our visual experiment can be downloaded from [15].…”

“…The displayed stimuli consisted of two masks and one mask with added target in one of the chrominance channels. The noise target was cropped from the same 510×510 noise target used in the previous gray-scale experiment by Alam et al [12]. It is a normalized log-Gabor noise patch with vertical orientation and 5 cycles per degree (cpd) spatial frequency for the selected viewing distance and monitor resolution.…”

“…We used first-order entropy, where the probability distribution was approximated with a 256-bins histogram. The RMS contrast of the masks was calculated as in [12] for each of the three YCbCr channels. To calculate edge density we used Laplacian of Gaussian (LoG) edge detector (of size 13x13 pixels) for the Y channel, with the threshold set to 0.002 and the standard deviation of the Gaussian set to σ=2.…”

“…Sensitivity to phase distortions was measured in grayscale natural images [9] and in color images [10], [11]. Alam et al measured the local detection thresholds for a log-Gabor noise target in natural grayscale images [12]. The images were blocksegmented and a masking map was obtained for all of the images from the CSIQ image database [13].…”