Andrew B. Watson

Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise

Studies of visual detection of a signal superimposed on one of two identical backgrounds show performance degradation when the background has high contrast and is similar in spatial frequency and/or orientation to the signal. To account for this finding, models include a contrast gain control mechanism that pools activity across spatial frequency, orientation and space to inhibit (divisively) the response of the receptor sensitive to the signal. In tasks in which the observer has to detect a known signal added to one of M different backgrounds grounds due to added visual noise, the main sources of degradation are the stochastic noise in the image and the suboptimal visual processing. We investigate how these two sources of degradation (contrast gain control and variations in the background) interact in a task in which the signal is embedded in one of M locations in a complex spatially varying background (structured background). We use backgrounds extracted from patient digital medical images. To isolate effects of the fixed deterministic background (the contrast gain control) from the effects of the background variations, we conduct detection experiments with three different background conditions: (1) uniform background, (2) a repeated sample of structured background, and (3) different samples of structured background. Results show that human visual detection degrades from the uniform background condition to the repeated background condition and degrades even further in the different backgrounds condition. These results suggest that both the contrast gain control mechanism and the background random variations degrade human performance in detection of a signal in a complex, spatially varying background. A filter model and added white noise are used to generate estimates of sampling efficiencies, an equivalent internal noise, an equivalent contrast-gain-control-induced noise, and an equivalent noise due to the variations in the structured background.

Improved detection model for DCT coefficient quantization

A detection model is developed to predict visibility thresholds for discrete cosine transform coefficient quantization error, based on the luminance and chrominance of the error. The model is an extension of a previously proposed luminance-based model, and is based on new experimental data. In addition to the luminance-only predictions of the previous model, the new model predicts the detectability of quantization error in color space directions in which chrominance error plays a major role. This more complete model allows DCT coefficient quantization matrices to be designed for display conditions other than those of the experimental measurements: other display luminances, other veiling luminances, other spatial frequencies (different pixel sizes, viewing distances, and aspect ratios), and other color directions.

Models of human image discrimination predict object detection in natural backgrounds

Object detection involves looking for one of a large set of object subimages in a large set of background images. Image discrimination models predict the probability that an observer will detect a difference between two images. We find that discrimination models can predict the relative detectability of objects in different images, suggesting that these simpler models may be useful in some object detection applications. Six images of a vehicle in an otherwise natural setting were altered to remove the vehicle and mixed with the original image in various proportions. Nineteen observers rated the 24 images for the presence of a vehicle. The pattern of observer detectabilities for the different images was predicted by three discrimination models. A Cortex transform discrimination model, a contrast sensitivity function filter model, and a root-mean-square difference predictor based on the digital image values gave prediction errors of 15%, 49%, and 46%, respectively. Two observers given the same images repeatedly to make the task a discrimination task rated the images similarly, but had detectabilities a factor of two higher.

Discrete cosine transform (DCT) basis function visibility: effects of viewing distance and contrast masking

Several recent image compression standards rely upon the discrete cosine transform (DCT). Models of DCT basis function visibility can be used to design quantization matrices for arbitrary viewing conditions and images. Here we report new results on the effects of viewing distance and contrast masking on basis function visibility. We measured contrast detection thresholds for DCT basis functions at viewing distances yielding 16, 32, and 64 pixels/degree. Our detection model has been elaborated to incorporate the observed effects. We have also measured detection thresholds for individual basis functions when superimposed upon another basis function of the same or a different frequency. We find considerable masking between nearby DCT frequencies. A model for these masking effects also is presented.