Results 281 to 290 of about 83,436 (308)

Information and Entropy Measurements on Video Sequences

2005 5th International Conference on Information Communications & Signal Processing, 2006
Video compression techniques usually involve lossy compression algorithms, with much of the original image data being discarded because it is not essential for human perception. However, lossy compression not only reduces data volume substantially, it also removes a reference for how well the remaining data has been compressed. Entropy measurements can
K. Kawaharada, K. Ohzeki, U. Speidel
openaire   +1 more source

Measuring Informativeness of Data by Entropy and Variance

1999
Measuring informativeness of data or news is particularly important as it quantifies the amount of “learning”. This is central to scientific progress as well as to assessing the direction and value of “information” and technologies. As is the case with all “indices”, the desirability of any measure of information depends on at least two considerations:
Nader Ebrahimi, Esfandiar Maasoumi, Ehsan S. Soofi   +2 more
openaire   +1 more source

Measuring cosmic inhomogeneities with information entropy

AIP Conference Proceedings, 2013
In standard cosmology, the global dynamics of the universe is assumed to be described by a homogeneous and isotropic FLRW universe model, but a realistic universe model should include local inhomogeneities, and the physical properties of such a realistic model averaged over a sufficiently large scale do not necessarily coincide with those of the FLRW ...
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Information Measurement and Entropy in Information-Knowledge Certainty

2019
This chapter is devoted to discussions on techniques and methods of information measurement relative to entropy in information-knowledge certainty within the conditions of the system of source-destination dualities. It follows the discussion in Chap.
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Information and entropy of countable measurable partitions. I

Kybernetika, 1974
Summary: This paper proposes a general definition of information function by axiomatic conditions: (a\(_1\)) \(F\) is a continuous function of \(p\in (0,1]\). (a\(_2\)) \(F(\tfrac 12)=F(1), F(1)=0\). (a\(_3\)) \(F(pq)=\varphi(F(p),F(q))\) where \(\varphi\) is a polynomial of its arguments.
Minaketan Behara, Prem Nath
openaire   +2 more sources

Global Sensitivity Analysis Based on Entropy: From Differential Entropy to Alternative Measures

Entropy, 2021
Zdenek Kala, Kala Zdenek
exaly  

Entropy and information measures in combinatorial optimization

Proceedings of the 1992 ACM/SIGAPP Symposium on Applied computing: technological challenges of the 1990's, 1992
Owen Murphy, Aby Tehranipour
openaire   +1 more source

Box-cox-sparse-measures-based blind filtering: Understanding the difference between the maximum kurtosis deconvolution and the minimum entropy deconvolution

Mechanical Systems and Signal Processing, 2022
Cristian Lopez, Dong Wang, Keegan J Moore   +2 more
exaly  

A history of graph entropy measures

Information Sciences, 2011
Matthias Dehmer, Abbé Mowshowitz
exaly  

MEASURES OF ENTROPY IN THE THEORY OF EVIDENCE

International Journal of General Systems, 1988
Serafin Moral
exaly