Sphere-covering, measure concentration, and source coding

IEEE Transactions on Information Theory

Volumn 47, Issue 4, 2001, Pages 1544-1552

  Kontoyiannis, I ^a  

^a BROWN UNIVERSITY   (United States)

Author keywords

Data compression; Large deviations; Measure concentration; Sphere covering

Indexed keywords

DATA COMPRESSION; ENCODING (SYMBOLS); GEOMETRY; PROBABILITY DENSITY FUNCTION; PROBABILITY DISTRIBUTIONS; SET THEORY; THEOREM PROVING;

MEASURE CONCENTRATION; RATE DISTORTION CODEBOOKS; SHANNON THEOREM; SOURCE CODING; SPHERE COVERING; STEIN LEMMA;

INFORMATION THEORY;

EID: 0035333502     PISSN: 00189448     EISSN: None     Source Type: Journal    
DOI: 10.1109/18.923735     Document Type: Letter

References (19)

1. Hypothesis testing and information theory
2. A large deviation theory proof of the abstract alphabet source coding theorem
3. Optimal numberings and isoperimetric problems on graphs
4. Pointwise redundancy in lossy data compression and universal ossy data compression
5. Efficient sphere-covering and converse measure concentration via generalized coding theorems
6. On the method of bounded differences
7. Concentration
8. Coding theorems for a discrete source with a fidelity criterion
9. Asymptotische Abschäungen in Shannons Informationstheorie
10. Concentration of measure and isoperimetric inequalities in product spaces
11. On the redundancy of lossy source coding with abstract alphabets