Slepian–Wolf coding theorem

E641830

coding theorem information theory theorem

The Slepian–Wolf coding theorem is a fundamental result in information theory that characterizes the limits of lossless data compression for correlated sources encoded separately but decoded jointly.

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Observed surface forms (1)

Surface form	Occurrences
Slepian–Wolf bound	1

Statements (49)

Predicate	Object
instanceOf	coding theorem ⓘ information theory theorem ⓘ
appliesTo	discrete memoryless sources ⓘ stationary ergodic sources (with extensions) ⓘ
assumes	joint decoder ⓘ known joint distribution of sources at encoder and decoder (in classical formulation) ⓘ separate encoders ⓘ two or more correlated discrete memoryless sources ⓘ
characterizes	rate region for lossless compression of correlated sources ⓘ
concerns	correlated information sources ⓘ distributed source coding ⓘ lossless data compression ⓘ separate encoding and joint decoding ⓘ
field	information theory ⓘ
generalizationOf	lossless source coding to distributed encoders ⓘ
guarantees	arbitrarily small probability of decoding error for rates in achievable region ⓘ
hasApplicationIn	Wyner–Ziv coding NERFINISHED ⓘ compressing correlated data streams ⓘ distributed sensor networks ⓘ multiterminal source coding ⓘ network information theory ⓘ
hasCodingApproach	LDPC code based Slepian–Wolf coding ⓘ syndrome-based coding using linear channel codes ⓘ turbo code based Slepian–Wolf coding ⓘ
implies	correlation can be exploited at the decoder ⓘ no rate loss compared to joint encoding for lossless compression ⓘ
influenced	correlation-aware compression algorithms ⓘ development of distributed video coding ⓘ
inspired	practical Slepian–Wolf codes based on channel codes ⓘ
introducedIn	1973 ⓘ
involves	asymptotically long block lengths ⓘ
isSpecialCaseOf	multiterminal source coding theory ⓘ
namedAfter	David Slepian NERFINISHED ⓘ Jack Wolf NERFINISHED ⓘ
publishedIn	IEEE Transactions on Information Theory NERFINISHED ⓘ
rateConstraint	R_X + R_Y ≥ H(X,Y) ⓘ R_X ≥ H(X\|Y) ⓘ R_Y ≥ H(Y\|X) ⓘ
relatedTo	Shannon source coding theorem NERFINISHED ⓘ Wyner–Ziv theorem NERFINISHED ⓘ network coding ⓘ
shows	side information at the decoder is sufficient for optimal compression rates ⓘ
states	each individual rate must be at least conditional entropy given the other source ⓘ sum of individual rates must be at least joint entropy of sources ⓘ
usesConcept	conditional entropy ⓘ entropy ⓘ joint entropy ⓘ joint typicality decoding ⓘ typical sequences ⓘ

Referenced by (2)

Full triples — surface form annotated when it differs from this entity's canonical label.

David Slepian → notableWork → Slepian–Wolf coding theorem ⓘ

David Slepian → notableConcept → Slepian–Wolf coding theorem ⓘ

this entity surface form: Slepian–Wolf bound