27,762 research outputs found
A Generative Model of Natural Texture Surrogates
Natural images can be viewed as patchworks of different textures, where the
local image statistics is roughly stationary within a small neighborhood but
otherwise varies from region to region. In order to model this variability, we
first applied the parametric texture algorithm of Portilla and Simoncelli to
image patches of 64X64 pixels in a large database of natural images such that
each image patch is then described by 655 texture parameters which specify
certain statistics, such as variances and covariances of wavelet coefficients
or coefficient magnitudes within that patch.
To model the statistics of these texture parameters, we then developed
suitable nonlinear transformations of the parameters that allowed us to fit
their joint statistics with a multivariate Gaussian distribution. We find that
the first 200 principal components contain more than 99% of the variance and
are sufficient to generate textures that are perceptually extremely close to
those generated with all 655 components. We demonstrate the usefulness of the
model in several ways: (1) We sample ensembles of texture patches that can be
directly compared to samples of patches from the natural image database and can
to a high degree reproduce their perceptual appearance. (2) We further
developed an image compression algorithm which generates surprisingly accurate
images at bit rates as low as 0.14 bits/pixel. Finally, (3) We demonstrate how
our approach can be used for an efficient and objective evaluation of samples
generated with probabilistic models of natural images.Comment: 34 pages, 9 figure
A Multiscale Pyramid Transform for Graph Signals
Multiscale transforms designed to process analog and discrete-time signals
and images cannot be directly applied to analyze high-dimensional data residing
on the vertices of a weighted graph, as they do not capture the intrinsic
geometric structure of the underlying graph data domain. In this paper, we
adapt the Laplacian pyramid transform for signals on Euclidean domains so that
it can be used to analyze high-dimensional data residing on the vertices of a
weighted graph. Our approach is to study existing methods and develop new
methods for the four fundamental operations of graph downsampling, graph
reduction, and filtering and interpolation of signals on graphs. Equipped with
appropriate notions of these operations, we leverage the basic multiscale
constructs and intuitions from classical signal processing to generate a
transform that yields both a multiresolution of graphs and an associated
multiresolution of a graph signal on the underlying sequence of graphs.Comment: 16 pages, 13 figure
A statistical reduced-reference method for color image quality assessment
Although color is a fundamental feature of human visual perception, it has
been largely unexplored in the reduced-reference (RR) image quality assessment
(IQA) schemes. In this paper, we propose a natural scene statistic (NSS)
method, which efficiently uses this information. It is based on the statistical
deviation between the steerable pyramid coefficients of the reference color
image and the degraded one. We propose and analyze the multivariate generalized
Gaussian distribution (MGGD) to model the underlying statistics. In order to
quantify the degradation, we develop and evaluate two measures based
respectively on the Geodesic distance between two MGGDs and on the closed-form
of the Kullback Leibler divergence. We performed an extensive evaluation of
both metrics in various color spaces (RGB, HSV, CIELAB and YCrCb) using the TID
2008 benchmark and the FRTV Phase I validation process. Experimental results
demonstrate the effectiveness of the proposed framework to achieve a good
consistency with human visual perception. Furthermore, the best configuration
is obtained with CIELAB color space associated to KLD deviation measure
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