Showing papers by "Eero P. Simoncelli published in 2004"

Image quality assessment: from error visibility to structural similarity

Abstract: Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

Image Quality Assessment: From Error Measurement to Structural Similarity

Abstract: Objective methods for assessing perceptual im- age quality traditionally attempt to quantify the visibility of errors (dierences) between a distorted image and a ref- erence image using a variety of known properties of the hu- man visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative com- plementary framework for quality assessment based on the degradation of structural information. As a specific exam- ple of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive ex- amples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MatLab imple- mentation of the proposed algorithm is available online at http://www.cns.nyu.edu/~lcv/ssim/.

Characterization of Neural Responses with Stochastic Stimuli

Abstract: A fundamental goal of sensory systems neuroscience is the characterization of the functional relationship between environmental stimuli and neural response. The purpose of such a characterization is to elucidate the computation being performed by the system. Qualitatively, this notion is exemplified by the concept of the “receptive field”, a quasi-linear description of a neuron’s response properties that has dominated sensory neuroscience for the past 50 years. Receptive field properties are typically determined by measuring responses to a highly restricted set of stimuli, parameterized by one or a few parameters. These stimuli are typically chosen both because they are known to produce strong responses, and because they are easy to generate using available technology.

Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model

Abstract: We examine a cascade encoding model for neural response in which a linear filtering stage is followed by a noisy, leaky, integrate-and-fire spike generation mechanism. This model provides a biophysically more realistic alternative to models based on Poisson (memoryless) spike generation, and can effectively reproduce a variety of spiking behaviors seen in vivo. We describe the maximum likelihood estimator for the model parameters, given only extracellular spike train responses (not intracellular voltage data). Specifically, we prove that the log-likelihood function is concave and thus has an essentially unique global maximum that can be found using gradient ascent techniques. We develop an efficient algorithm for computing the maximum likelihood solution, demonstrate the effectiveness of the resulting estimator with numerical simulations, and discuss a method of testing the model's validity using time-rescaling and density evolution techniques.

Differentiation of discrete multidimensional signals

Abstract: We describe the design of finite-size linear-phase separable kernels for differentiation of discrete multidimensional signals. The problem is formulated as an optimization of the rotation-invariance of the gradient operator, which results in a simultaneous constraint on a set of one-dimensional low-pass prefilter and differentiator filters up to the desired order. We also develop extensions of this formulation to both higher dimensions and higher order directional derivatives. We develop a numerical procedure for optimizing the constraint, and demonstrate its use in constructing a set of example filters. The resulting filters are significantly more accurate than those commonly used in the image and multidimensional signal processing literature.

Stimulus synthesis for efficient evaluation and refinement of perceptual image quality metrics

Abstract: We propose a methodology for comparing and refining perceptual image quality metrics based on synthetic images that are optimized to best differentiate two candidate quality metrics. We start from an initial distorted image and iteratively search for the best/worst images in terms of one metric while constraining the value of the other to remain fixed. We then repeat this, reversing the roles of the two metrics. Subjective test on the quality of pairs of these images generated at different initial distortion levels provides a strong indication of the relative strength and weaknesses of the metrics being compared. This methodology also provides an efficient way to further refine the definition of an image quality metric.

Machine Learning Applied to Perception: Decision Images for Gender Classification

Abstract: We study gender discrimination of human faces using a combination of psychophysical classification and discrimination experiments together with methods from machine learning. We reduce the dimensionality of a set of face images using principal component analysis, and then train a set of linear classifiers on this reduced representation (linear support vector machines (SVMs), relevance vector machines (RVMs), Fisher linear discriminant (FLD), and prototype (prot) classifiers) using human classification data. Because we combine a linear preprocessor with linear classifiers, the entire system acts as a linear classifier, allowing us to visualise the decision-image corresponding to the normal vector of the separating hyperplanes (SH) of each classifier. We predict that the female-to-maleness transition along the normal vector for classifiers closely mimicking human classification (SVM and RVM [1]) should be faster than the transition along any other direction. A psychophysical discrimination experiment using the decision images as stimuli is consistent with this prediction.

Constraining a Bayesian Model of Human Visual Speed Perception

Abstract: It has been demonstrated that basic aspects of human visual motion perception are qualitatively consistent with a Bayesian estimation framework, where the prior probability distribution on velocity favors slow speeds. Here, we present a refined probabilistic model that can account for the typical trial-to-trial variabilities observed in psychophysical speed perception experiments. We also show that data from such experiments can be used to constrain both the likelihood and prior functions of the model. Specifically, we measured matching speeds and thresholds in a two-alternative forced choice speed discrimination task. Parametric fits to the data reveal that the likelihood function is well approximated by a LogNormal distribution with a characteristic contrast-dependent variance, and that the prior distribution on velocity exhibits significantly heavier tails than a Gaussian, and approximately follows a power-law function.