Classification and prediction of wavelet coefficients for lossless compression of landsat images

Inspired by previous work on the modelling of wavelet coefficients, and on the observed differences between distributions of wavelet coefficients belonging to different landscapes, we present a lossless compressor of multispectral images based on the prediction of wavelet coefficients, conditioned t...

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Autor principal: Acevedo, D.
Otros Autores: Ruedin, A.
Formato: Acta de conferencia Capítulo de libro
Lenguaje:Inglés
Publicado: 2006
Acceso en línea:Registro en Scopus
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100 1 |a Acevedo, D. 
245 1 0 |a Classification and prediction of wavelet coefficients for lossless compression of landsat images 
260 |c 2006 
270 1 0 |m Acevedo, D.; Departamento de Computatión, FCEyN, Pab. I, CP 1428, Ciudad de Buenos Aires, Argentina; email: dacevedo@dc.uba.ar 
506 |2 openaire  |e Política editorial 
504 |a Wu, N.M.X., Context-based, adaptive, lossless image coding (1997) IEEE Transactions on Communications, 45 (4), pp. 437-444 
504 |a Weinberger, G.S.M., Sapiro, G., The loco-i lossless image compression algorithm: Principles and standardization into jpeg-ls (2000) IEEE Transactions on Image Processing, 9, pp. 1309-1324 
504 |a Zandi, E.S.A., Allen, J., Boliek, M., Crew: Compression with reversible embedded wavelets (1995) Proceedings. DCC 1995 Data Compression Conference, pp. 212-221 
504 |a Said, A., Pearlman, W., A new fast and efficient image codec based on set partitioning in hierarchical trees (1996) IEEE Transactions on Circuits and Systems for Video Technology, 6, pp. 243-250 
504 |a Skodras, A., Christopoulos, C., Ebrahimi, T., The jpeg 2000 still image compression standard (2001) IEEE Signal Processing Magazine, 18, pp. 36-58. , September 
504 |a Wu, N.M.X., Context-based lossless interband compression -extending CALIC (2000) IEEE Transactions on Image Processing, 9, pp. 994-1001 
504 |a Yang, K., Faryar, A., A contex-based predictive coder for lossless and near-lossless compression of video (2000) Proceedings of International Conference on Image Processing, 1, pp. 144-147 
504 |a Tang, X., Cho, S., Pearlman, W.A., Comparison of 3d set partitioning methods in hy- Perspectral image compression featuring an improved 3d-spiht (2003) Proceedings of the Data Compression Conference, pp. 449-449 
504 |a Lee, H.S., Younan, N.-H., King, R.L., Hyperspectral image cube compression combining jpeg 2000 and spectral decorrelation (2000) Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 6, pp. 3317-3319 
504 |a Motta, G., Rizzo, F., Storer, J.A., Partitioned vector quantization: Application to lossless compression of hyperspectral images (2003) Proceedings International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 
504 |a Lin, C.-T., Lee, Y.-C., Pu, H.-C., Satellite sensor image classification using cascaded architecture of neural fuzzy network (2000) IEEE Transactions on Geoscience and Remote Sensing, 38 (2), pp. 1033-1043 
504 |a Chae, H., Kim, S., Ryu, J., A classification of multitemporal landsat tm data using principal component analysis and artificial neural network (1997) IEEE Proc. Intnl. Geoscience and Remote Sensing Symposium IGARSS '971, pp. 517-520 
504 |a Provost, J.-N., Collet, C., Rostaing, P., Pérez, P., Bouthemy, P., Hierarchical markovian segmentation of multispectral images for the reconstruction of water depth maps (2004) Elsevier, Computer Vision and Image Understanding 93 (2004) 155174, 93, pp. 155-174 
504 |a Rizzo, G.M.F., Carpentieri, B., Storer, J.A., Low complexity lossless compression of hyperspectral imagery via linear prediction (2005) IEEE Signal Processing Letters, 12 (2), pp. 138-141 
504 |a Tang, C., Cheung, N.-M., Ortega, A., Raghavendra, C.S., Efficient inter-band prediction and wavelet based compression for hyperspectral imagery: A distributed source coding approach (2005) Data Compression Conference, pp. 437-446 
504 |a Ruedin, A., Acevedo, D., Prediction of coefficients for lossless compression of multispectral images (2005) "Satellite Data Compression, Communications, and Archiving", Proceedings of SPIE, 5889, pp. 202-211 
504 |a Acevedo, D.G., Ruedin, A.M.C., Reduction of interband correlation for landsat image compression (2005) Brazilian Symposium on Computer Graphics and Image Processing 
504 |a Daubechies, I., (1992) Ten Lectures on Wavelets, , Society for Industrial and Applied Mathematics 
504 |a Strang, G., Nguyen, T., (1996) Wavelets and Filter Banks, , Wellesley Cambridge Press 
504 |a Calderbank, W.S.A.R., Daubechies, I., Yeo, B., Wavelet transforms that map integer to integers (1998) Applied and Computational Harmonics Analysis, 5 (3), pp. 332-369 
504 |a Said, A., Pearlman, W., An image multiresolution representation for lossless and lossy compression (1996) IEEE Trans Image Proc, 5 (9) 
504 |a Buccigrossi, R., Simoncelli, E., Image compression via joint statistical characterization in the wavelet domain (1999) IEEE Trans Signal Proc, 8, pp. 1688-1701 
504 |a Tate, S.R., Band ordering in lossless compression of multispectral images (1997) IEEE Transactions on Computers, 46 (4), pp. 477-483A4 - SPIE 
520 3 |a Inspired by previous work on the modelling of wavelet coefficients, and on the observed differences between distributions of wavelet coefficients belonging to different landscapes, we present a lossless compressor of multispectral images based on the prediction of wavelet coefficients, conditioned to the landscape. This compressor operates blockwise. The wavelet transform is applied to each block, and detail coefficients from the two finest scales are predicted by means of a linear combination of other coefficients, which may belong to the same band as the predicted coefficient, or to a previously coded band. The weights for the lineal combination are estimated on-line: for each detail subband, the compressor is trained on all the detail coefficients belonging to the same class. In addition, a different band ordering is considered for each block. Differences in prediction are coded with a conditional entropy coder. Preliminary results reveal that we obtain more accurate predictions.  |l eng 
593 |a Departamento de Computatión, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina 
593 |a Departamento de Computatión, FCEyN, Pab. I, CP 1428, Ciudad de Buenos Aires, Argentina 
690 1 0 |a CLASSIFICATION 
690 1 0 |a LOSSLESS 
690 1 0 |a MULTISPECTRAL 
690 1 0 |a PREDICTION 
690 1 0 |a WAVELET COEFFICIENTS 
690 1 0 |a CLASSIFICATION (OF INFORMATION) 
690 1 0 |a CODES (SYMBOLS) 
690 1 0 |a ENTROPY 
690 1 0 |a WAVELET TRANSFORMS 
690 1 0 |a CODED BAND 
690 1 0 |a LANDSAT IMAGES 
690 1 0 |a MULTISPECTRAL IMAGES 
690 1 0 |a WAVELET COEFFICIENTS 
690 1 0 |a IMAGE COMPRESSION 
700 1 |a Ruedin, A. 
711 2 |c San Diego, CA  |d 13 August 2006 through 14 August 2006  |g Código de la conferencia: 68482 
773 0 |d 2006  |g v. 6300  |p Proc SPIE Int Soc Opt Eng  |n Proceedings of SPIE - The International Society for Optical Engineering  |x 0277786X  |z 0819463795  |z 9780819463791  |t Satellite Data Compression, Communications, and Archiving II 
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