Projection-Based Spatially Adaptive Reconstruction of Block-Transform Compressed Images

IEEE Transactions on Image Processing

Volumn 4, Issue 7, 1995, Pages 896-908

  Yang, Yongyi ^a   Galatsanos, Nikolas P ^a   Katsaggelos, Aggelos K ^b  

^a ILLINOIS INSTITUTE OF TECHNOLOGY   (United States)

^b NORTHWESTERN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE ALGORITHMS; BANDWIDTH; COMPUTATIONAL COMPLEXITY; COMPUTER SYSTEM RECOVERY; CONSTRAINT THEORY; DECODING; IMAGE CODING; IMAGE RECONSTRUCTION; SET THEORY; STATISTICAL METHODS; TECHNOLOGY; VLSI CIRCUITS;

ADAPTIVE IMAGE RECOVERY ALGORITHM; BLOCK DISCRETE COSINE TRANSFORM; BLOCK TRANSFORM CODING; IMAGE RECOVERY PROBLEM; PROJECTION ONTO CONVEX SETS;

IMAGE COMPRESSION;

EID: 0029341202     PISSN: 10577149     EISSN: 19410042     Source Type: Journal    
DOI: 10.1109/83.392332     Document Type: Article

References (29)

1. Committee Draft ISO/IEC CD 10918-1, “Title: Digital compression and coding of continuous-tone still images, Part 1: Requirements and guidelines,” Mar. 15, 1991.
2. W. B. Pennebaker and J. L. Mitchell, JPEG Still Image Data Compression Standard. New York: Van Nostrand Reinhold, 1992.
3. H. C. Reeves and J. S. Lim, “Reduction of blocking effects in image coding,” Opt. Eng., vol. 23, no. 1, pp. 34-37, Jan./Feb. 1984.
4. B. Ramamurthi and A. Gersho, “Nonlinear space-variant postprocessing of block coded images,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 34, no. 5, pp. 1258-1267, Oct. 1986.
5. K. Sauer, “Enhancement of low bit-rate coded images using edge detection and estimation,” Comput. Vision Graphics Image Processing: Graphical Models Image Processing, vol. 53, no. 1, pp. 52-62, Jan. 1991.
6. R. Rosenholtz and A. Zakhor, “Iterative procedures for reduction of blocking effects in transform image coding,” IEEE Trans. Circuits Syst. [Video Technol. J, vol. 2, no. 1, pp. 91-94, Mar. 1992.
7. L. G. Gubin, B. T. Polyak, and E. V. Raid, “The method of projections for finding the common point of convex sets,” U.S.S.R. Comput. Math. Mathematic. Phys., vol. 7, no. 6, pp. 1-24 1967.
8. D. C. Youla, “Generalized image restoration by the method of alternating orthogonal projections,” IEEE Trans. Circuits Syst. [Video Technol. J, vol. 25, no. 9, pp. 694-702, Sept. 1978.
9. S. J. Reeves and S. L. Eddins, “Comments on iterative procedures for reduction of blocking effects in transform image coding,” IEEE Trans. Circuit Syst. [Video Technol]., vol. 3, no. 6, pp. 439-440, Dec. 1993.
10. R. L. Stevenson, “Reduction of coding artifacts in transform image coding,” in Proc. Int. Conf. Acoust., Speech, Signal Processing, Minneapolis, MN, 1993, pp. 401-404.
11. Y. Yang, N. P. Galatsanos, and A. K. Katsaggelos, “Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images,” IEEE Trans. Circuits Syst. [Video Technol]., vol. 3, no. 6, pp. 421-432, Dec. 1993.
12. “Iterative projection algorithms for removing the blocking artifacts of block-DCT compressed images,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Minneapolis, Apr. 1993, pp. V.405-V.408.
13. T. Ozcelik, J. Brailean, and A. Katsaggelos, “Image and video compression algorithms based on recovery techniques using mean field annealing,” Proc. IEEE, vol. 83, no. 2, pp. 304-316, Feb. 1995.
14. D. Reininger, “Iterative post-processing algorithms for reducing block artifacts in low-bit rate DCT coded images,” M.S.E.E. thesis, ECE Dept. Illinois Inst. of Tech., Dec. 1991.
15. D. C. Youla and H. Webb, “Image restoration by the method of convex projections: Part 1—Theory,” IEEE Trans. Med. Imaging, vol. 1, no. 2, pp. 81-94, Oct. 1982.
16. H. Stark, Ed., Image Recovery: Theory and Application. New York: Academic, 1987.
17. P. L. Combettes, “The foundations of set theoretic estimation,” Proc. IEEE, vol. 81, no. 2, pp. 182-208, Feb. 1993.
18. F. C. Jeng and J. W. Woods, “Compound Gauss-Markov random fields for image restoration,” IEEE Trans. Signal Processing, vol. 39, no. 3, pp. 683-697, 1991.
19. K. Sauer and C. Bouman, “A local update strategy for iterative reconstruction from projections,” IEEE Trans. Signal Processing, vol. 41, no. 2, pp. 534-548, Feb. 1993.
20. C. Bouman and K. Sauer, “A generalized Gaussian image model for edge-preserving MAP estimation,” IEEE Trans. Image Processing, vol. 2, no. 3, pp. 296-310, July 1993.
21. G. Anderson and A. Netravali, “Image restoration based on a subjective criterion,” IEEE Trans. Syst., Man, Cybern., vol. 6, no. 12, pp. 845-853, Dec. 1976.
22. A. K. Katsaggelos, J. Biemond, R. M. Mersereau, and R. W. Schafer, “Nonstationary iterative image restoration,” in Proc. ICASSP-85, Mar. 1985, pp. 696-699.
23. A. K. Katsaggelos, “A general formulation of adaptive iterative image restoraton algorithms,” in Proc. 1986 Conf. Inform. Sciences Syst., Princeton, NJ, March 1986, pp. 42-47.
24. R. L. Lagendijk, J. Biemond, and D. E. Boekee, “Regularized iterative restoration with ringing reduction,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 36, no. 12, pp. 1874-1888, Dec. 1988.
25. A. K. Katsaggelos, J. Biemond, R. M. Mersereau, and R. W. Schafer, “A regularized iterative image restoration algorithm,” IEEE Trans. Signal Processing, vol. 39, no. 4, pp. 914-929, Apr. 1991.
26. H. J. Trussell and M. R. Civanlar, “The feasible solution in signal restoration,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 32, no. 2, pp. 201-212, Apr. 1984.
27. B. G. Lee, “A new algorithm to compute the discrete cosine transform,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 32, no. 6, pp. 1243-1245, Dec. 1984.
28. Y. Yang and N. Galatsanos, “Edge-preserving reconstruction of compressed images using projections and a divide-and-conquer strategy,” Proc. IEEE-ICIP, pp. 535-539, Austin, TX, 1994.
29. “Projection-based decoding of low bit-rate MPEG data,” in Proc. SPIE, VCIP-94, Chicago, Sept. 1994, vol. 2308, pp. 331-342, Chicago, IL, Sept. 1994.