Image Coding

Remote sensing images are becoming more and more important for a large number of applications. The growing interest is motivated by several concurring reasons, one of the most important being the improved quality of state-of-the art sensors which deliver images with very high spatial, spectral, and radiometric resolution. However, together with this wealth of information comes the problem of managing such a huge amount of data which must be transmitted to the ground station on limited-capacity channels, archived for long periods of time, and finally disseminated to the end users on common transmission facilities. Therefore, suitable compression algorithms are highly desirable in all the phases of the data lifetime and can play a central role for the success of remote-sensing applications, as is testified by the growing bulk of related scientific literature, and by the attention paid to this topic by national space agencies.

 

AuthorsTitleJournal/ConferenceDateFile
G. Poggi, D. Cozzolino and L. Verdoliva Self Organizing Maps for the design of Multiple Description Vector QuantizersNeurocomputing 2013 PDF
A. Greco, G. Poggi, L. Verdoliva and S. ServaEffects of Compression on SAR image interpretabilityIEEE Gold Remote Sensing Conference 2010
G. Poggi and L. VerdolivaDesign of Low-Resolution Multiple Description Vector Quantizers by means of the Self Organizing MapsEuropean Signal Processing Conference (EUSIPCO) 2008 PDF
S. Parrilli, G. Poggi and L. VerdolivaA SPIHT-like Image Coder Based on the Contourlet TransformIEEE International Conference on Image Processing (ICIP) 2008 PDF
M. Cagnazzo, G. Poggi and L. VerdolivaRegion-based transform coding of multispectral imagesIEEE Transactions on Image Processing 2007 PDF
M. Cagnazzo, S. Parrilli, G. Poggi and L. VerdolivaImproved Class-Based Coding of Multispectral Images With Shape-Adaptive Wavelet TransformIEEE Geoscience and Remote Sensing Letters 2007 PDF
M. Cagnazzo, S. Parrilli, G. Poggi and L. VerdolivaCosts and Advantages of Object-Based Image Coding with Shape-Adaptive Wavelet TransformEURASIP Journal on Image and Video Processing 2007 PDF
M. Cagnazzo, L. Cicala, G. Poggi and L. VerdolivaLow-complexity compression of multispectral images based on classified transform codingSignal Processing: Image Communication 2006 PDF
M. Cagnazzo, R. Gaetano, S. Parrilli and L. VerdolivaAdaptive Region-based Compression of Multispectral ImagesIEEE International Conference on Image Processing (ICIP) 2006 PDF
M. Cagnazzo, R. Gaetano, S. Parrilli and L. VerdolivaRegion based Compression of Multispectral Images by Classified KLTEuropean Signal Processing Conference (EUSIPCO) 2006 PDF
M. Cagnazzo, L. Cicala, G. Poggi, G. Scarpa and L. VerdolivaAn Unsupervised Segmentation-based Coder for Multispectral ImagesEuropean Signal Processing Conference (EUSIPCO) 2005 PDF
M. Cagnazzo, G. Poggi and L. VerdolivaA Comparison of Flat and Object-based Transform Coding Techniques for the Compression of Multispectral ImagesIEEE International Conference on Image Processing (ICIP) 2005 PDF
M. Cagnazzo, G. Poggi and L. VerdolivaCosts and Advantages of Shape-Adaptive Wavelet Transform for Region-based Image CodingIEEE International Conference on Image Processing (ICIP) 2005 PDF
M. Cagnazzo, G. Poggi, G. Scarpa and L. VerdolivaCompression of multitemporal remote sensing images through Bayesian segmentationIEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2004 PDF
M. Cagnazzo, G. Poggi, L. Verdoliva and A. ZinicolaRegion-oriented Compression of Multispectral Images by Shape-Adaptive Wavelet Transform and SPIHTIEEE International Conference on Image Processing (ICIP) 2004 PDF
M. Cagnazzo, G. Poggi and L. VerdolivaThe advantage of segmentation in SAR image compressionIEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2002 PDF
C. D'Elia, G. Poggi and L. VerdolivaCompression of SAR Raw Data Through Range Focusing and Variable-Rate Trellis-Coded QuantizationIEEE Transactions on Image Processing 2001 PDF
G. Poggi, A. R. P. Ragozini and L. VerdolivaCompression of SAR Data Through Range Focusing and Variable-Rate Vector QuantizationIEEE Transactions on Geoscience and Remote Sensing 2000 PDF
C. D'Elia, G. Poggi and L. VerdolivaCompression of SAR raw data via range focusing and trellis coded quantizationIEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2000 PDF
P. Dragotti, G. Poggi and A. R. P. RagoziniCompression of Multispectral Images by Three-Dimensional SPIHT AlgorithmIEEE Transactions on Geoscience and Remote Sensing 2000 PDF
G. Poggi, A. R. P. Ragozini and L. VerdolivaOn-board compression of SAR data through range focusingIEEE International Geoscience and Remote Sensing Symposium (IGARSS) 1999 PDF
G. Gelli and G. PoggiCompression of multispectral images by spectral classification and transform codingIEEE Transactions on Image Processing 1999 PDF
G. R. Canta and G. PoggiKronecker-product gain-shape vector quantization for multispectral and hyperspectral image codingIEEE Transactions on Image Processing 1998 PDF
G. R. Canta and G. PoggiCompression of multispectral images by address-predictive vector quantizationSignal Processing: Image Communication 1997 PDF