Name
Abstract | ||
|---|---|---|
This paper proposes a compression algorithm relying on a classified linear-regression prediction followed by context based modeling and arithmetic coding of the outcome residuals. Images are partitioned into blocks, e.g., 8×8 or 16×16, and a minimum mean square (MMSE) linear predictor is calculated for each block. Fuzzy clustering is utilized to reduce the number of such predictors. Given a preset number of classes, a Fuzzy-C-Means algorithm produces an initial guess of classified predictors to be fed to an iterative procedure which classifies pixel blocks simultaneously refining the associated predictors. All the predictors are transmitted along with the label of each block. Coding time is affordable thanks to fast convergence of the iterative algorithms. Decoding is always performed in real time. The compression scheme provides impressive performances, especially when applied to X-ray images |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/ISCAS.1999.779930 | Circuits and Systems, 1999. ISCAS '99. Proceedings of the 1999 IEEE International Symposium |
Keywords | Field | DocType |
arithmetic codes,data compression,fuzzy set theory,image coding,iterative methods,prediction theory,X-ray images,arithmetic coding,classified linear-regression prediction,coding time,compression algorithm,context based modeling,fuzzy-clustered prediction,image partitioning,iterative procedure,lossless image compression,minimum mean square linear predictor,outcome residuals,pixel blocks | Fuzzy clustering,Control theory,Computer science,Iterative method,Fuzzy logic,Algorithm,Theoretical computer science,Linear prediction,Data compression,Cluster analysis,Arithmetic coding,Lossless compression | Conference |
Volume | ISBN | Citations |
4 | 0-7803-5471-0 | 1 |
PageRank | References | Authors |
0.50 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| B. Aiazzi | 1 | 528 | 49.43 |
| Baronti, S. | 2 | 176 | 8.26 |
| Luciano Alparone | 3 | 1 | 0.50 |