In this paper I present a distortion metric for color video sequences. It is based on a contrast gain control model of the human visual system that incorporates spatial and temporal aspects of vision as well as color perception. The model achieves a close fit to contrast sensitivity and contrast masking data from several different psychophysical experiments for both luminance and color stimuli. The metric is used to assess the quality of MPEG-coded sequences.

Lossy compression algorithms used in digital video systems produce artifacts whose visibility strongly depends on the actual image content. Simple error measures such as RMSE or PSNR, albeit popular, ignore this important fact and are only a mediocre predictor of perceived quality. Many applications require more reliable assessment methods. This paper discusses issues in vision modeling for perceptual video quality assessment (PVQA). Its purpose is not to describe a particular model or system, but rather to summarize and to provide pointers to up-to-date knowledge of important characteristics of the human visual system, to explain how these characteristics may be incorporated in vision models for PVQA, to give a brief overview of the state-of-the-art and current efforts in this field, and to outline directions for future research.

The psychometric function relates an observer’s performance to an independent variable, usually some physical quantity of a stimulus in a psychophysical task. This paper, together with its companion paper (Wichmann & Hill, 2001), describes an integrated approach to (1) fitting psychometric functions, (2) assessing the goodness of fit, and (3) providing confidence intervals for the function’s parameters and other estimates derived from them, for the purposes of hypothesis testing. The present paper deals with the first two topics, describing a constrained maximum-likelihood method of parameter estimation and developing several goodness-of-fit tests. Using Monte Carlo simulations, we deal with two specific difficulties that arise when fitting functions to psychophysical data. First, we note that human observers are prone to stimulus-independent errors (or lapses). We show that failure to account for this can lead to serious biases in estimates of the psychometric function’s parameters and illustrate how the problem may be overcome. Second, we note that psychophysical data sets are usually rather small by the standards required by most of the commonly applied statistical tests. We demonstrate the potential errors of applying traditional c 2 methods to psychophysical data and advocate use of Monte Carlo resampling techniques that do not rely on asymptotic theory. We have made available the software to implement our methods. The performance of an observer on a psychophysical

We have documented previously a close relationship between neuronal activity in the middle temporal visual area (MT or V5) and behavioral judgments of motion (Newsome et al., 1989; Salzman et al., 1990; Britten et al., 1992; Britten et al., 1996). We have now used numerical simulations to try to understand how neural signals in area MT support psychophysical decisions. We developed a model that pools neuronal responses drawn from our physiological data set and compares average responses in different pools to produce psychophysical decisions. The structure of the model allows us to assess the relationship between “neuronal ” input signals and simulated psychophysical performance using the same methods we have applied to real experimental data. We sought to reconcile three experimental observations: psychophysical performance (threshold sensitivity to motion

Improvements in measuring thresholds, or points on a psychometric function, have advanced the field of psychophysics in the last 30 years. The arrival of laboratory computers allowed the introduction of adaptive procedures, where the presentation of the next stimulus depends on previous responses of the subject. Unfortunately, these procedures present themselves in a bewildering variety, though some of them differ only slightly. Even someone familiar with several methods cannot easily name the differences, or decide which method would be best suited for a particular application. This review tries to illuminate the historical background of adaptive procedures, explain their differences and similarities, and provide criteria for choosing among the various techniques. Psychometric functions Psychophysical threshold Binary responses Sequential estimate Efficiency Yes-no methods Forced-choice methods

A Monte Carlo method for computing the bias and standard deviation of estimates of the parameters of a psychometricfunction such as the WeibulllQuick is described. The method, based on Efron’s parametric bootstrap, can also be used to estimate confidence intervals for these parameters. The method’s ability to predict bias, standard deviation, and confidence intervals is evaluated in two ways. First, its predictions are compared tothe outcomes of Monte Carlo simulations ofpsychophysical experiments. Second, its predicted confidenceintervals were compared with the actual variability of human observers in a psychophysical task. Computer programs implementing the method are available from the author. The performance of an observer in a detection or discrimination task is typically summarized by fitting a psychometric function to the data. Examples of fitting methods include probit analysis (Finney, 1971) and maximum-likelihood fits using the Weibull/Quick psychometric function (Quick, 1974; Watson, 1979; Weibull, 1951). These methods retain an estimate of threshold and

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Models of spatial vision usually assume a `front-end' of spatial-frequency and orientation selective channels. Subthreshold-summation studies have provided some of the strongest support for this notion. We applied a single-channel energy model and a multiple-channels probability-summation model to explore subthreshold-summation phenomena. We measured the contrast thresholds for detection of two superimposed Gabor patches as a function of the spatial-frequency and orientation difference between the components. The stimuli were centred 7.5 deg above the fixation point and were windowed by a Gaussian function with one of two different spatial spreads. We have shown that the spatial-frequency and orientation selectivity in subthreshold summation of Gabor patches is determined by the similarity (cross-correlation) between the stimulus components. A single-channel energy model as well as a multiple-channels probability-summation model could explain the summation data.

We assessed, in a task where subjects had to detect smooth deviations from circularity, whether the underlying mechanisms were localised in space to the size of the individual perturbations or whether they computed global shape. By manipulating the phase, the number of cycles of modulation and the spatial arrangement of the perturbations we argue that although either aspect can be detected, performance is ultimately limited by a global shape detecting mechanism. We show that this global mechanism receives input from spatially coarse, crossed orientationally tuned filters whose peak position in orientation depends on the overall shape to be detected. 1999 Elsevier Science Ltd. All rights reserved. Keywords: Circularity; Local; Global; Filtering; Orientation www.elsevier.com/locate/visres 1. Introduction In principle, shape descriptions can be built up from single local estimates using filters matched to parts of objects or by more global estimates using large filters operating on the...

Contrast sensitivity is a measure of the ability of an observer to detect contrast signals of particular spatial and temporal frequencies. Here I derive a formal definition of contrast sensitivity that can be applied to individual linear visual neurons. A neuron is modeled by a contrast transfer function and its modulus, contrast gain, and by a noise power spectrum. The distributions of neural responses to signal and blank presentations are derived, and from these, a definition of contrast sensitivity is obtained. This formal definition may be used to relate the sensitivities of various populations of neurons, and to relate the sensitivities of neurons to that of the behaving animal. key words: contrast sensitivity, maintained discharge, noise, signal detection theory, linear systems, power spectrum INTRODUCTION One of the fundamental goals of vision science is to relate the performance of the human observer to the behavior of visual neurons. Performance has many dimensions, but one ...