3,200 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
Packing a Knapsack of Unknown Capacity
We study the problem of packing a knapsack without knowing its capacity.
Whenever we attempt to pack an item that does not fit, the item is discarded;
if the item fits, we have to include it in the packing. We show that there is
always a policy that packs a value within factor 2 of the optimum packing,
irrespective of the actual capacity. If all items have unit density, we achieve
a factor equal to the golden ratio. Both factors are shown to be best possible.
In fact, we obtain the above factors using packing policies that are universal
in the sense that they fix a particular order of the items and try to pack the
items in this order, independent of the observations made while packing. We
give efficient algorithms computing these policies. On the other hand, we show
that, for any alpha>1, the problem of deciding whether a given universal policy
achieves a factor of alpha is coNP-complete. If alpha is part of the input, the
same problem is shown to be coNP-complete for items with unit densities.
Finally, we show that it is coNP-hard to decide, for given alpha, whether a set
of items admits a universal policy with factor alpha, even if all items have
unit densities
GermEval 2014 Named Entity Recognition Shared Task: Companion Paper
This paper describes the GermEval 2014 Named Entity Recognition (NER) Shared Task workshop at KONVENS. It provides background information on the motivation of this task, the data-set, the evaluation method, and an overview of the participating systems, followed by a discussion of their results. In contrast to previous NER tasks, the GermEval 2014 edition uses an extended tagset to account for derivatives of names and tokens that contain name parts. Further, nested named entities had to be predicted, i.e. names that contain other names. The eleven participating teams employed a wide range of techniques in their systems. The most successful systems used state-of-the- art machine learning methods, combined with some knowledge-based features in hybrid systems
GermEval 2014 Named Entity Recognition Shared Task: Companion Paper
This paper describes the GermEval 2014 Named Entity Recognition (NER) Shared Task workshop at KONVENS. It provides background information on the motivation of this task, the data-set, the evaluation method, and an overview of the participating systems, followed by a discussion of their results. In contrast to previous NER tasks, the GermEval 2014 edition uses an extended tagset to account for derivatives of names and tokens that contain name parts. Further, nested named entities had to be predicted, i.e. names that contain other names. The eleven participating teams employed a wide range of techniques in their systems. The most successful systems used state-of-the- art machine learning methods, combined with some knowledge-based features in hybrid systems
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