Name
Abstract | ||
|---|---|---|
The practical lossless digital image compressors that achieve the best results in terms of compression ratio are also simple and fast algorithms with low complexity both in terms of memory usage and running time. Surprisingly, the compression ratio achieved by these systems cannot be substantially improved even by using image-by-image optimization techniques or more sophisticate and complex algorithms [6]. A year ago, B. Meyer and P. Tischer were able, with their TMW [2], to improve some current best results (they do not report results for all test images) by using global optimization techniques and multiple blended linear predictors. Our investigation is directed to determine the effectiveness of an algorithm that uses multiple adaptive linear predictors, locally optimized on a pixel-by-pixel basis. The results we obtained on a test set of nine standard images are encouraging, where we improve over CALIC on some images. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/DCC.1999.755699 | Data Compression Conference |
Keywords | Field | DocType |
adaptive codes,computational complexity,data compression,image coding,linear predictive coding,optimisation,complexity,compression ratio,digital image compressors,local optimization,lossless image coding,multiple adaptive linear prediction | Global optimization,Computer science,Linear prediction,Theoretical computer science,Digital image,Compression ratio,Data compression,Linear predictive coding,Test set,Lossless compression | Conference |
ISSN | ISBN | Citations |
1068-0314 | 0-7695-0096-X | 5 |
PageRank | References | Authors |
1.19 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giovanni Motta | 1 | 88 | 8.98 |
| James A. Storer | 2 | 931 | 156.06 |
| Bruno Carpentieri | 3 | 256 | 32.41 |