18,750 research outputs found
Simulated Annealing for JPEG Quantization
JPEG is one of the most widely used image formats, but in some ways remains
surprisingly unoptimized, perhaps because some natural optimizations would go
outside the standard that defines JPEG. We show how to improve JPEG compression
in a standard-compliant, backward-compatible manner, by finding improved
default quantization tables. We describe a simulated annealing technique that
has allowed us to find several quantization tables that perform better than the
industry standard, in terms of both compressed size and image fidelity.
Specifically, we derive tables that reduce the FSIM error by over 10% while
improving compression by over 20% at quality level 95 in our tests; we also
provide similar results for other quality levels. While we acknowledge our
approach can in some images lead to visible artifacts under large
magnification, we believe use of these quantization tables, or additional
tables that could be found using our methodology, would significantly reduce
JPEG file sizes with improved overall image quality.Comment: Appendix not included in arXiv version due to size restrictions. For
full paper go to:
http://www.eecs.harvard.edu/~michaelm/SimAnneal/PAPER/simulated-annealing-jpeg.pd
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
Shearlet-based compressed sensing for fast 3D cardiac MR imaging using iterative reweighting
High-resolution three-dimensional (3D) cardiovascular magnetic resonance
(CMR) is a valuable medical imaging technique, but its widespread application
in clinical practice is hampered by long acquisition times. Here we present a
novel compressed sensing (CS) reconstruction approach using shearlets as a
sparsifying transform allowing for fast 3D CMR (3DShearCS). Shearlets are
mathematically optimal for a simplified model of natural images and have been
proven to be more efficient than classical systems such as wavelets. Data is
acquired with a 3D Radial Phase Encoding (RPE) trajectory and an iterative
reweighting scheme is used during image reconstruction to ensure fast
convergence and high image quality. In our in-vivo cardiac MRI experiments we
show that the proposed method 3DShearCS has lower relative errors and higher
structural similarity compared to the other reconstruction techniques
especially for high undersampling factors, i.e. short scan times. In this
paper, we further show that 3DShearCS provides improved depiction of cardiac
anatomy (measured by assessing the sharpness of coronary arteries) and two
clinical experts qualitatively analyzed the image quality
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