24,671 research outputs found
Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data
Image data are increasingly encountered and are of growing importance in many
areas of science. Much of these data are quantitative image data, which are
characterized by intensities that represent some measurement of interest in the
scanned images. The data typically consist of multiple images on the same
domain and the goal of the research is to combine the quantitative information
across images to make inference about populations or interventions. In this
paper we present a unified analysis framework for the analysis of quantitative
image data using a Bayesian functional mixed model approach. This framework is
flexible enough to handle complex, irregular images with many local features,
and can model the simultaneous effects of multiple factors on the image
intensities and account for the correlation between images induced by the
design. We introduce a general isomorphic modeling approach to fitting the
functional mixed model, of which the wavelet-based functional mixed model is
one special case. With suitable modeling choices, this approach leads to
efficient calculations and can result in flexible modeling and adaptive
smoothing of the salient features in the data. The proposed method has the
following advantages: it can be run automatically, it produces inferential
plots indicating which regions of the image are associated with each factor, it
simultaneously considers the practical and statistical significance of
findings, and it controls the false discovery rate.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS407 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Steerable Discrete Cosine Transform
In image compression, classical block-based separable transforms tend to be
inefficient when image blocks contain arbitrarily shaped discontinuities. For
this reason, transforms incorporating directional information are an appealing
alternative. In this paper, we propose a new approach to this problem, namely a
discrete cosine transform (DCT) that can be steered in any chosen direction.
Such transform, called steerable DCT (SDCT), allows to rotate in a flexible way
pairs of basis vectors, and enables precise matching of directionality in each
image block, achieving improved coding efficiency. The optimal rotation angles
for SDCT can be represented as solution of a suitable rate-distortion (RD)
problem. We propose iterative methods to search such solution, and we develop a
fully fledged image encoder to practically compare our techniques with other
competing transforms. Analytical and numerical results prove that SDCT
outperforms both DCT and state-of-the-art directional transforms
Perceptually-Driven Video Coding with the Daala Video Codec
The Daala project is a royalty-free video codec that attempts to compete with
the best patent-encumbered codecs. Part of our strategy is to replace core
tools of traditional video codecs with alternative approaches, many of them
designed to take perceptual aspects into account, rather than optimizing for
simple metrics like PSNR. This paper documents some of our experiences with
these tools, which ones worked and which did not. We evaluate which tools are
easy to integrate into a more traditional codec design, and show results in the
context of the codec being developed by the Alliance for Open Media.Comment: 19 pages, Proceedings of SPIE Workshop on Applications of Digital
Image Processing (ADIP), 201
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