Name
Abstract | ||
|---|---|---|
In this paper, we present a novel technique that uses the optimal linear prediction theory to exploit all the existing redundancies in a color video sequence for lossless compression purposes. The main idea is to introduce the spatial, the spectral, and the temporal correlations in the autocorrelation matrix estimate. In this way, we calculate the cross correlations between adjacent frames and adjacent color components to improve the prediction, i.e., reduce the prediction error energy. The residual image is then coded using a context-based Golomb-Rice coder, where the error modeling is provided by a quantized version of the local prediction error variance. Experimental results show that the proposed algorithm achieves good compression ratios and it is roboust against the scene change problem. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TIP.2008.2003391 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
data compression,image colour analysis,image sequences,matrix algebra,prediction theory,video coding,autocorrelation matrix estimation,color video sequence,context-based Golomb-Rice coder,lossless compression,optimal linear prediction theory,spatial correlations,spectral correlations,temporal correlations,Lossless compression,optimal linear prediction | Residual,Computer vision,Linear system,Pattern recognition,Autocorrelation matrix,Linear prediction,Compression ratio,Artificial intelligence,Data compression,Mathematics,Lossless compression,Color image | Journal |
Volume | Issue | ISSN |
17 | 11 | 1057-7149 |
Citations | PageRank | References |
1 | 0.40 | 16 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefano Andriani | 1 | 1 | 0.40 |
| Giancarlo Calvagno | 2 | 260 | 23.63 |