Lossless compression of multi/hyper-spectral imagery based on a 3-D fuzzy prediction

Abstract

This paper describes an original application of fuzzy logic to the reversible compression of multispectral data. The method consists of a space-spectral varying prediction followed by context-based classification and arithmetic coding of the outcome residuals. Prediction of a pixel to be encoded is obtained from the fuzzy-switching of a set of linear regression predictors. Pixels both on the current band and on previously encoded bands may be used to define a causal neighborhood. The coefficients of each predictor are calculated so as to minimize the mean-squared error for those pixels whose intensity level patterns lying on the causal neighborhood, belong in a fuzzy sense to a predefined cluster. The size and shape of the causal neighborhood, as well as the number of predictors to be switched, may be chosen by the user and determine the tradeoff between coding performances and computational cost. The method exhibits impressive results, thanks to the skill of predictors in fitting multispectral data patterns, regardless of differences in sensor responses.

Progressive 3-D Coding of Hyperspectral Images Based on JPEG 2000

“…PSNR has been used as quality metric for lossy compression. 1 JPEG 2000 has been run without error resilience options, and no quality layers have been formed.…”

“…Lossless compression is typically carried out by means of prediction-based approaches such as those in [1]- [5]. More recently, there has been an increasing interest in the lossy compression of multispectral and hyperspectral images.…”

Adaptive multispectral GPU accelerated architecture for Earth Observation satellites

“…As well as low complexity specific algorithms, several high computational complexity algorithms that feature advanced pre-processing have been proposed [15] [16][17] [18]. These algorithms are able to achieve state-of-the-art levels of compression due to the incorporation of image segmentation to determine areas of homogenous pixels for increased prediction accuracy.…”

Spectral Distortion in Lossy Compression of Hyperspectral Data

“…The latter indicates that the decompressed data have a user-defined maximum absolute error, being zero in the lossless case. Several variants exist in prediction schemes, the most performing being adaptive [2][3][4][5][6]. Lossless compression thoroughly preserves the information of the data but allows a moderate decrement in transmission bit rate to be achieved.…”