On the amount of statistical side information required for lossy data compression

IEEE Transactions on Information Theory

Volumn 43, Issue 4, 1997, Pages 1112-1121

  Merhav, Neri ^a,b   Ziv, Jacob ^a,b  

^a IEEE   (Israel)

^b TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

Convergence rates; Empirical vector quantizer design; Rate distortion; Universal lossy source coding; Vector quantization

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; DATA COMPRESSION; ENCODING (SYMBOLS); SIGNAL DISTORTION; STATISTICAL METHODS;

RATE DISTORTION;

VECTOR QUANTIZATION;

EID: 0031191355     PISSN: 00189448     EISSN: None     Source Type: Journal    
DOI: 10.1109/18.605571     Document Type: Article

References (6)

1. I. Csiszár and J. Körner, Information Theory: Coding Theorems for Discrete Memoryless Systems. New York: Academic, 1981.
2. T. M. Cover and J. A. Thomas, Elements of Information Theory. New York: Wiley, 1991.
3. Y. Hershkovits and J. Ziv, "On fixed-database universal data compression with limited memory," submitted for publication.
4. T. Linder, G. Lugosi, and K. Zeger, "Rates of convergence in the source coding theorem, in empirical quantizer design, and in universal lossy source coding," IEEE Trans. Inform. Theory, vol. 40, pp. 1728-1740, Nov. 1994.
5. D. Pollard, "Quantization and the method of k-means," IEEE Trans. Inform. Theory, vol. IT-28, pp. 199-205, Mar. 1982.
6. A. D. Wyner and J. Ziv, "Classification with finite memory," IEEE Trans. Inform. Theory, vol. 42, pp. 337-347, Mar. 1996.