Prediccion De Coefficientes For Lossless Compression Of Multispectral Images

Prediction of coefficients for Lossless Compression of Multispectral Images
Ana M. C. Ruedin and Daniel G. Acevedo Departamento de Computaci´n, o Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires
ABSTRACT
We present a lossless compressor for multispectral Landsat images that exploits interband and intraband correlations. The compressor operates on blocks of 256 × 256 pixels,and performs two kinds of predictions. For bands 1, 2, 3, 4, 5, 6.2 and 7, the compressor performs an integer-to-integer wavelet transform, which is applied to each block separately. The wavelet coefficients that have not yet been encoded are predicted by means of a linear combination of already coded coefficients that belong to the same orientation and spatial location in the same band, andcoefficients of the same location from other spectral bands. A fast block classification is performed in order to use the best weights for each landscape. The prediction errors or differences are finally coded with an entropy - based coder. For band 6.1, we do not use wavelet transforms, instead, a median edge detector is applied to predict a pixel, with the information of the neighbouring pixels and theequalized pixel from band 6.2. This technique exploits better the great similarity between histograms of bands 6.1 and 6.2. The prediction differences are finally coded with a context-based entropy coder. The two kinds of predictions used reduce both spatial and spectral correlations, increasing the compression rates. Our compressor has shown to be superior to the lossless compressors Winzip, LOCO-I, PNGand JPEG2000. Keywords: lossless, compression, multispectral, median edge detector, wavelet

1. INTRODUCTION
By designing methods for efficient satellite image compression, we intend to reduce transmission and storage expenses, thereby improving communications involving large data volumes. The high correlation observed between some bands of multispectral images have not been taken in considerationin the design of a compressor for such images. To our knowledge, no available satellite image coder exploits the inter band correlations of multispectral images in order to improve compression. Landsat 7 images are eight-band multispectral images obtained from spectrally filtering radiation at visible (bands 1,2,3), near-infrared (band 4), short-wave infrared (bands 5,7), and thermal (bands6.1,6.2) frequency bands from the sun-lit earth. The 8-band Landsat7 image typically requires 400 MB to be stored. The storage of original satellite images imposes in addition one nontrivial condition: compression must be lossless. We present our lossless compressor for multispectral images, it operates on blocks of 256 × 256 pixels, and performs two kinds of predictions. In section 2 we introduce theinteger-to-integer wavelet transform that we apply on blocks of the image, for bands 1, 2, 3, 4, 5, 6.2 and 7. In section 3 we explain our approach for linear prediction of the wavelet coefficients, which is followed by entropy-based encoding of the prediction errors. Our original contribution consists of making predictions with the coefficients belonging to other spectral bands that have already beenencoded, in order to reduce the entropy of these errors, by taking advantage of the correlations between bands. We also tune the weights for the linear prediction according to the landscape being processed, which requires a fast block classification.
E-mail: anita@dc.uba.ar

In section 4 we explain our median edge predictor applied to compress band 6.1: it takes into consideration the pixelsfrom the highly correlated band 6.2, this band must previously be equalized. In section 5 we have our numerical results and concluding remarks.

2. WAVELET TRANSFORMS
Discrete dyadic wavelet transforms have been successfully applied to solve different problems in many fields, owing to the good spatial localization and fairly good frequency localization of their bases.1, 2 They are invertible and...