Pasquale S. Alba

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

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.

Three-dimensional lossless compression based on a separable generalized recursive interpolation

In this work, it is shown that the generalized recursive interpolation (GRINT) proposed is the most effective progressive technique for inter-frame reversible compression of tomographic sections that typically occur in the medical field. An image sequence is decimated by a factor 2, first along rows only, then along columns only, and eventually along slices only, recursively in a sequel, thus creating a gray-level hyperpyramid whose number of voxels halves at every level. The top of the pyramid (root) is stored and then directionally interpolated by means of a 1D kernel. Interpolation errors with the underlying equally-sized hyperlayer are stored as well. The same procedure is repeated, until the image sequence is completely decomposed. The advantage of the novel scheme with respect to other noncausal DPCM schemes is twofold: firstly interpolation is performed from all error-free values, thereby reducing the variance of residuals; secondly different correlation values along rows, columns and sections can be exploited for a better decorrelation.

Reversible compression of multispectral imagery based on an enhanced inter-band JPEG prediction

A modified inter-band scheme based on the predictors used by lossless JPEG is proposed for lossless data compression of multispectral images. Basically, the value of the current pixel in the current band is predicted by the best JPEG predictor on the previously encoded band. Improvements are achieved by considering also the corresponding prediction error on the previous band. Coding performances, assessed on a variety of Landsat TM data, are better by about 3% than those of the basic inter-band scheme, and by 7% than those attained by lossless JPEG.

Lossless image compression based on a fuzzy linear prediction with context based entropy coding

A novel method for reversible compression of 2D and 3D data is presented. It consists of a spatial 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 predictors, i.e., yielding linear combinations of surrounding pixels. The coefficients of each predictor are calculated to minimize prediction MSE for pixels belonging to a cluster in the hyperspace of gray level patterns lying on a preset causal neighborhood. In the 3D case, pixels both on the current slice and on previously encoded slices may be used. The size and shape of the causal neighborhood, as well as the number of predictors to be switched, may be chosen before running the algorithm to determine the trade-off between coding performances and computational cost. The method exhibits impressive performances, for both 2D and 3D data, mainly thanks to the optimality of predictors, due to their skill in fitting patterns of data.

Reversible inter-frame compression of multispectral images based on a previous-closest-neighbor prediction

Previous closest neighbor (PCN) prediction has prevously been proposed for lossless data compression of multispectral images, in order to take advantage of inter-band data correlation. The basic idea to predict the value of the current pixel in the current band on the basis of the best zero-order predictor on the previously coded band, has been applied by extending the set of predictors to those adopted by lossless JPEG. Performances increase more than 5% when passing from the original set of predictors to the extended set.

A distributed implementation of fuzzy clustering and switching of linear regression models for lossless compression of imagery and 3D data

A distributed implementation of a new method for reversible compression of both 2D and 3D data is presented. A classified prediction is first trained through fuzzy clustering; then, data decorrelation is accomplished by prediction in a fuzzy fashion. Context-based adaptive arithmetic coding is tailored to the prediction errors to enhance entropy coding. Results and comparisons with other schemes are presented and discussed together with computational issues.

Reversible compression of 2D and 3D data through a fuzzy linear prediction with context-based arithmetic coding

A novel method for reversible compression of 2D and 3D data is presented. An adaptive spatial prediction is followed by a context-based classification with arithmetic coding of the outcome residuals. Prediction of a pixel to be encoded is obtained from the fuzzy-switching of a set of linear predictors. The coefficients of each predictor are calculated to minimize prediction MSE for pixels belonging to a cluster in the hyperspace of graylevel patterns lying on a preset causal neighborhood. In the 3D cases, piles both on the current slice and on previously encoded slices may be used. The size and shape of the causal neighborhood, as well as the number of predictors to be switched, may be chosen before running the algorithm and determine the trade-off between coding performances and computational cost. The method exhibits impressive performances, for both 2D and 3D data, mainly thanks to the optimality of predictors, due to their skill in fitting data patterns.

Fuzzy clustering and soft switching of linear regression models for reversible image compression

This paper describes an original application of fuzzy logic to reversible compression of 2D and 3D data. The compression method consists of a space-variant prediction followed by context- based classification ad 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. The coefficients of each predictor are calculated so as to minimize prediction MSE for those pixels whose graylevel patterns, lying on a causal neighborhood of prefixed shape, are vectors belonging in a fuzzy sense to one cluster. In the 3D case, pixels both on the current slice and on previously encoded slices may be used. The size and shape of the causal neighborhood, as well as the number of predictors to be switched, may be chosen before running the algorithm and determine the trade-off between coding performance sand computational cost. The method exhibits impressive performances, for both 2D and 3D data, mainly thanks to the optimality of predictors, due to their skill in fitting data patterns.

Lossless image compression based on a generalized recursive interpolative DPCM

It is shown that the generalized recursive interpolation algorithm (GRINT) proposed is the most effective hierarchical technique for reversible compression of images that typically occur in remote sensing. The main advantage of the novel scheme with respect to other noncausal DPCM schemes is that interpolation is performed from all error-free values, thereby reducing the variance of residuals. Tests on LandSat, NOAA/AVHRR, MeteoSat, and SPOT images show that GRINT outperforms established hierarchical techniques, and also lossless JPEG and optimum DPCM when dealing with SPOT data, with the further advantage that GRINT makes error-free tokens available at any resolution, thereby expediting remote browsing on large image data-bases.

Lossless compression of medical images based on an enhanced generalized multidimensional S-Transform

A variant of the S-transform (ST), which is a multiresolution Walsh-Hadamard transform having the structure of a dyadic wavelet decomposition, is proposed for both speeding up computation,and enabling extension to 3D data, when reversible coding of medical images and image sequences is concerned. It is derived by exploiting the same parity of the sum and the difference of two integers in a separable fashion, and thereby it has been easily extended to decorrelate volumetric data. Also, the spatial structure of the ST is considered by modelling the statistics of the different subbands of integer coefficients as generalized Gaussian probability density functions (PDF), and by fitting individual codebooks for variable length coding. The estimate of the shape factor of the PDF is based on a novel criterion matching the entropy of the theoretical and actual distributions. Coding performance comparisons are made with a similar algorithm, like the reduced-difference pyramid (RDP), designed for the purpose of hierarchical lossless image compression,as well as with lossless JPEG. Tests carried out on medical images and tomographic sequences show improvements of the proposed scheme over both the RDP and the 2D ST. Archival/retrieval are feasible on-line, still with the benefits of multiresolution coding for telebrowsing.