E
Eero P. Simoncelli
Researcher at Center for Neural Science
Publications - 273
Citations - 83270
Eero P. Simoncelli is an academic researcher from Center for Neural Science. The author has contributed to research in topics: Wavelet & Image processing. The author has an hindex of 81, co-authored 260 publications receiving 68623 citations. Previous affiliations of Eero P. Simoncelli include New York University & Stanford University.
Papers
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Journal ArticleDOI
Partitioning neuronal variability
TL;DR: A model in which spikes are generated by a Poisson process whose rate is the product of a drive that is sensory in origin and a gain summarizing stimulus-independent modulatory influences on excitability provides an accurate account of response distributions of visual neurons in macaque lateral geniculate nucleus and cortical areas V1, V2 and MT.
Proceedings Article
Scale Mixtures of Gaussians and the Statistics of Natural Images
TL;DR: In this paper, the authors examined properties of the class of Gaussian scale mixtures, and showed that these densities can accurately characterize both the marginal and joint distributions of natural image wavelet coefficients.
Posted Content
End-to-end Optimized Image Compression
TL;DR: In this article, a nonlinear analysis transformation, a uniform quantizer, and a non-linear synthesis transformation are used to optimize the entire model for rate-distortion performance over a database of training images.
Proceedings ArticleDOI
Reduced-reference image quality assessment using a wavelet-domain natural image statistic model
Zhou Wang,Eero P. Simoncelli +1 more
TL;DR: This paper proposes an RR image quality assessment method based on a natural image statistic model in the wavelet transform domain that uses the Kullback-Leibler distance between the marginal probability distributions of wavelet coefficients of the reference and distorted images as a measure of image distortion.
Journal ArticleDOI
Spatiotemporal Elements of Macaque V1 Receptive Fields
TL;DR: This analysis reveals an unsuspected richness of neuronal computation within V1, where simple and complex cell responses are best described using more linear filters than the one or two found in standard models.