Quantization as histogram segmentation: Globally optimal scalar quantizer design in network systems

Data Compression Conference Proceedings

Volumn 2002-January, Issue , 2002, Pages 302-311

  Muresan, D ^a   Effros, M ^b  

^a Stanford University   (United States)

^b CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Algorithm design and analysis; Data compression; Decoding; Design optimization; Histograms; Intelligent networks; Partitioning algorithms; Polynomials; Quantization; Shortest path problem

Indexed keywords

ALGORITHMS; DECODING; DESIGN; DIRECTED GRAPHS; ENTROPY; GRAPH THEORY; GRAPHIC METHODS; IMAGE CODING; INTELLIGENT NETWORKS; OPTIMAL SYSTEMS; OPTIMIZATION; POLYNOMIAL APPROXIMATION; POLYNOMIALS;

ALGORITHM DESIGN AND ANALYSIS; DESIGN OPTIMIZATION; HISTOGRAMS; PARTITIONING ALGORITHMS; QUANTIZATION; SHORTEST PATH PROBLEM;

DATA COMPRESSION;

EID: 2642516351     PISSN: 10680314     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DCC.2002.999968     Document Type: Conference Paper

References (5)

1. J. D. Bruce. Optimum Quantization. PhD thesis, M.I.T., Cambridge, May 1964.
2. D. K. Sharma. Design of absolutely optimal quantizers for a wide class of distortion measures. IEEE Trans. on Information Theory, IT-24(6):693-702, 1978.
3. X. Wu. Algorithmic approach to mean-square quantization. PhD thesis, University of Calgary, 1988.
4. X. Wu and K. Zhang. Quantizer monotonicities and globally optimal scalar quantizer design. IEEE Trans. on Information Theory, IT-39(3):1049-1053, 1993.
5. D. Muresan and M. Effros. Quantization as histogram segmentation: globally optimal scalar quantizer design in network systems. 2001. In preparation.