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Lars-Erik Persson

dblp:02/2006 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1992
0009-0008-8835-292XORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1Theory of computation · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Coding theory · 67% Information theory · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information theory › information measures
entropy
0.011991
On quantizer distortion and the upper bound for exponential entropy · IEEE Trans. Inf. Theory 1991
Coding theory › source coding
quantization
0.011991
On quantizer distortion and the upper bound for exponential entropy · IEEE Trans. Inf. Theory 1991
Coding theory › source coding › quantization › quantization theory
quantization error
0.011991
On quantizer distortion and the upper bound for exponential entropy · IEEE Trans. Inf. Theory 1991
YearPublicationVenuePosition
1992 Some properties of generalized exponential entropies with applications to data compression
Timo Koski, Lars-Erik Persson
Inf. Sci.2
1991 On quantizer distortion and the upper bound for exponential entropy
abstract
A sharp upper bound is derived for the exponential entropy in the class of absolutely continuous distributions with specific standard deviation and an exact description of the extremal distributions. This result is interpreted as determining the least favorable cases for certain methods of quantization of analog sources. It is known that for a large class of quantizers (both zero-memory and vector) the rth power distortion, as well as some other distortion criteria, are bounded below by a constant, depending on r, multiplied by a certain integral of the source's probability density. It is pointed out that this bound can be rewritten in terms of the exponential entropy. The exponential entropy measures the quantitative extent or range of the source distribution. This fact gives a physical interpretation of the indicated limits of quantizer performance, further elucidated by the main result.>
Timo Koski, Lars-Erik Persson
IEEE Trans. Inf. Theory2