Principles and practice of information theory
Principles and practice of information theory
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Coding of Waveforms: Principles and Applications to Speech and Video
Digital Coding of Waveforms: Principles and Applications to Speech and Video
Flexible Independent Component Analysis
Journal of VLSI Signal Processing Systems
Near-lossless image compression by relaxation-labelled prediction
Signal Processing - Image and Video Coding beyond Standards
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Generalized Gaussian modeling of correlated signal sources
IEEE Transactions on Signal Processing
Fuzzy logic-based matching pursuits for lossless predictive coding of still images
IEEE Transactions on Fuzzy Systems
On the modeling of DCT and subband image data for compression
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Interactive visualization of function fields by range-space segmentation
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
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This work focuses on estimating the information conveyed to a user by multi-dimensional digitised signals. The goal is establishing the extent to which an increase in radiometric resolution, or equivalently in signal-to-noise ratio (SNR), can increase the amount of information available to users. Lossless data compression is exploited to measure the "useful" information content of the 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, whose relevance is null to a user, and the intrinsic information of hypothetically noise-free samples. Once the parametric model of the noise, assumed to be possibly non-Gaussian, has been preliminarily estimated, the mutual information between noise-free signal and recorded noisy signal is easily estimated. However, it is desirable to know what is the amount of information that the digitised samples would convey if they were ideally recorded without observation noise. Therefore, an entropy model of the source is defined and such a model is inverted to yield an estimate of the information content of the noise-free source from the code rate and the noise model. Results are reported and discussed both on a simulated noisy image and on true hyperspectral data (220 spectral bands) recorded by the AVIRIS imaging spectrometer.