Tiling and adaptive image compression

IEEE Transactions on Information Theory

Volumn 46, Issue 5, 2000, Pages 1789-1799

  Lee, Wee Sun ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; IMAGE CODING; IMAGE SEGMENTATION; MATHEMATICAL MODELS; MATRIX ALGEBRA; PROBABILITY; VECTOR QUANTIZATION;

PROBABILITY MODELING; QUADTREE METHODS; SEQUENTIAL DATA COMPRESSION; SPECIALIST MODEL; UNIVERSAL SOURCE CODING;

IMAGE COMPRESSION;

EID: 0034250348     PISSN: 00189448     EISSN: None     Source Type: Journal    
DOI: 10.1109/18.857791     Document Type: Article

References (20)

1. A. Blum, "Empirical support for winnow and weighted-majority based algorithms: Results on a calendar scheduling domain," Machine Learning, vol. 26, pp. 5-23, 1997.
2. C. Chrysalis and A. Ortega, "Efficient context-based lossy wavelet image coding," in Proc. Data Compression Conf., Snowbird, UT, 1997.
3. W. Equitz and T. Cover, "Successive refinement of information," IEEE Trans. Inform. Theory, vol. 37, pp. 269-275, Mar. 1991.
4. Y. Freund, R. E. Schapire, Y. Singer, and M. Warmuth, "Using and combining predictors that specialize," in Proc. 29th Anna. ACM Symp. Theory of Computing, El Paso, TX, 1997.
5. R. M. Gray, Source Coding Theory. Norwell, MA: Kluwer, 1990.
6. D. P. Helmbold and R. E. Schapire, "Predicting nearly as well as the best pruning of a decision tree," Machine Learning, vol. 27, no. 1, pp. 51-68, 1997.
7. M. Herbster and M. Warmuth, "Tracking the best expert," Machine Learning, vol. 32, no. 2, Aug. 1998.
8. R. L. Joshi, H. Jafarkhani, J. H. Kasner, T. R. Fischer, N. Farvardin, M. W. Marcellin, and R. H. Bamberger, "Comparison of different methods of classification in subband coding of images," IEEE Trans. Image Processing, pp. 1473-1486, Nov. 1997.
9. W. S. Lee, "Trees, windows and tiles for wavelet image compression," in Data Compression Conf., Snowbird, UT, 2000.
10. S. M. LoPresto, K. Ramchandan, and M. T Orchard, "Image coding based on mixture modeling of wavelet coefficients and a fast estimationquantization framework," in Proc. Data Compression Conf., Snowbird, UT, 1997, pp. 221-230.
11. N. Merhav, "On the minimum description length principle for sources with piecewise constant parameters," IEEE Trans. Inform. Theory, vol. 39, pp. 1962-1967, Nov. 1993.
12. N. Merhav and M. Feder, "Universal prediction," IEEE Trans. Inform. Theory, vol. 44, pp. 2124-2147, Oct. 1999.
13. JPEG2000. Requirements and Profiles, version 6, ISO/IEC JTC/SC29/WG1 N1385, JPEG2000, July 1999.
14. A. Said and W. A. Pearlman, "A new, fast, and efficient image codec based on set partitioning in hierarchical trees," IEEE Trans. Circuits Syst. Video Technoi, vol. 6, pp. 243-249, Apr. 1996.
15. J. M. Shapiro, "Embedded image coding using zerotrees of wavelet coefficients," IEEE Trans. Signal Processing, vol. 41, pp. 3445-3462, Dec. 1993.
16. P. A. J. Volf and F. M. J. Willems, "Switching between two universal source coding algorithms," in Proc. Data Compression Conf., Snowbird, UT, 1998, pp. 491-500.
17. V. Vovk, "Derandomizing stochastic prediction strategies," in Proc. 10th Anna. Conf. Computational Learning Theory, Nashville, TN, 1997, pp. 32-43.
18. F. M. J. Willems, Y. M. Shtarkov, and T. J. Tjalken, "The context-tree weighting method: Basic properties," IEEE Trans. Inform.Theory, vol. 41, pp. 653-664, May 1995.
19. F. M. J. Willems, "Coding for a binary independent piecewise-identically-distributed source," IEEE Trans. Inform. Theory, vol. 42, pp. 2210-2217, Nov. 1996.
20. X. Wu, "Context quantization with Fisher discriminant for adaptive embedded wavelet image coding," in Proc. Data Compression Conf., Snowbird, UT, 1999, pp. 102-111.