Information-theoretic heterogeneity measurement for SAR imagery

A heterogeneity feature, calculable from synthetic aperture radar (SAR) images on a per-pixel basis, but relying on global image statistics, is defined and discussed. Starting from the multiplicative speckle and texture models relating the amount of texture and speckle to the local mean and variance at every pixel, such a feature is rigorously derived from Shannon's information theory as the conditional information of local standard deviation to local mean. Thanks to robust statistical estimation, it is very little sensitive to the noise affecting SAR data, and thus capable of capturing subtle variations of texture whenever they are embedded in a heavy speckle. Experimental results carried out on two SAR images with different degrees of noisiness demonstrate that the proposed feature is likely to be useful for a variety of automated segmentation and classification tasks.     

Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis

This paper compares two general and formal solutions to the problem of fusion of multispectral images with high-resolution panchromatic observations. The former exploits the undecimated discrete wavelet transform, which is an octave bandpass representation achieved from a conventional discrete wavelet transform by omitting all decimators and upsampling the wavelet filter bank. The latter relies on the generalized Laplacian pyramid, which is another oversampled structure obtained by recursively subtracting from an image an expanded decimated lowpass version. Both the methods selectively perform spatial-frequencies spectrum substitution from an image to another. In both schemes, context dependency is exploited by thresholding the local correlation coefficient between the images to be merged, to avoid injection of spatial details that are not likely to occur in the target image. Unlike other multiscale fusion schemes, both the present decompositions are not critically subsampled, thus avoiding possible impairments in the fused images, due to missing cancellation of aliasing terms. Results are presented and discussed on SPOT data.     

A reduced Laplacian pyramid for lossless and progressive imagecommunication

The Laplacian pyramid (LP) is appropriate for lossy image compression; conversely, the reduced-difference pyramid (RDP), having as many data as pixels, gives a better performance with lossless encoding. A reduced LP is designed by discarding the anti-aliasing filter and adopting a half-band interpolator, thus retaining three over four of the LP coefficients. Lossless coding outperforms both LP and RDP, especially when dealing with medical images     

Information-theoretic assessment of sampled hyperspectral imagers

This work focuses on estimating the information conveyed to a user by hyperspectral image data. The goal is establishing the extent to which an increase in spectral resolution enhances the amount of usable information. Indeed, a tradeoff exists between spatial and spectral resolution due to physical constraints of multi-band sensors imaging with a prefixed SNR. After describing an original method developed for the automatic estimation of variance and correlation of the noise introduced by hyperspectral imagers, lossless interband data compression is exploited to measure the useful information content of hyperspectral data. In fact, the bit rate achieved by the reversible compression process takes into account both the contribution of the “observation” noise (i.e., information regarded as statistical uncertainty, but whose relevance to a user is null) and the intrinsic information of radiance sampled and digitized through an ideally noise-free process. An entropic model of the decorrelated image source is defined and, once the parameters of the noise, assumed to be Gaussian and stationary, have been measured, such a model is inverted to yield an estimate of the information content of the noise-free source from the code rate. Results are reported and discussed on both simulated and AVIRIS data     

Fuzzy logic-based matching pursuits for lossless predictive coding of still images

This paper presents an application of fuzzy-logic techniques to the reversible compression of grayscale images. With reference to a spatial differential pulse code modulation (DPCM) scheme, prediction may be accomplished in a space-varying fashion either as adaptive, i.e., with predictors recalculated at each pixel, or as classified, in which image blocks or pixels are labeled in a number of classes, for which fitting predictors are calculated. Here, an original tradeoff is proposed; a space-varying linear-regression prediction is obtained through fuzzy-logic techniques as a problem of matching pursuit, in which a predictor different for every pixel is obtained as an expansion in series of a finite number of prototype nonorthogonal predictors, that are calculated in a fuzzy fashion as well. To enhance entropy coding, the spatial prediction is followed by context-based statistical modeling of prediction errors. A thorough comparison with the most advanced methods in the literature, as well as an investigation of performance trends and computing times to work parameters, highlight the advantages of the proposed fuzzy approach to data compression.     

Lossless image compression by quantization feedback in acontent-driven enhanced Laplacian pyramid

In this paper, the effects of quantization noise feedback on the entropy of Laplacian pyramids are investigated. This technique makes it possible for the maximum absolute reconstruction error to be easily and strongly upper-bounded (near-lossless coding), and therefore, allows reversible compression. The entropy-minimizing optimum quantizer is obtained by modeling the first-order distributions of the differential signals as Laplacian densities, and by deriving a model for the equivalent memoryless entropy. A novel approach, based on an enhanced Laplacian pyramid, is proposed for the compression, either lossless or lossy, of gray-scale images. Major details are prioritized through a content-driven decision rule embedded in a uniform threshold quantizer with noise feedback. Lossless coding shows improvements over reversible Joint Photographers Expert Group (JPEG) and the reduced-difference pyramid schemes, while lossy coding outperforms JPEG, with a significant peak signal-to-noise ratio (PSNR) gain. Also, subjective quality is higher even at very low bit rates, due to the absence of the annoying impairments typical of JPEG. Moreover, image versions having resolution and SNR that are both progressively increasing are made available at the receiving end from the earliest retrieval stage on, as intermediate steps of the decoding procedure, without any additional cost.