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Differential pulse code modulation (DPCM) is a widely used technique for both lossy and lossless compression of images. In this paper, the effect of using a nonlinear predictor based on artificial neural networks (ANN) for a DPCM encoder is investigated. The ANN predictor uses a 3-layer perceptron model with 3 input nodes, 30 hidden nodes, and 1 output node. The back-propagation learning algorithm is used for the training of the network. Simulation results are presented to compare the performance of the proposed ANN-based nonlinear predictor with that of a global linear predictor as well as an optimized minimum-mean-squared-error (MMSE) linear predictor. Preliminary computer simulations demonstrate that for a typical test image, the zeroth-order entropy of the differential (error) image can be reduced by more than 15% compared to the case where optimum linear predictors are employed. Some future research directions are also discussed.

We introduce the iteratively clustered interpolation algorithm to compute a structured printer inverse look-up table from irregularly sampled experimental color data. The algorithm is based on a gradient optimization method, with initial points generated through an iterative technique. Experimental results with a digital color printer are provided to illustrate the algorithm.

While the bispectrum has been used for the reconstruction of non-minimum phase 1-D signals not much work of a similar nature has been done for multidimensional signals. Here we present a technique for reconstructing 2-D non-minimum phase signals from samples of their bispectra. The reconstruction procedure involves recovering the magnitude of the Fourier transform of the signal from the bispectrum magnitude and its phase from the bispectrum phase. By using the bispectrum the ambiguity regarding phase is considerably removed (up to a linear phase factor) when compared to the spectral factorization approach.

The problem of reconstructing a moving object from multiple snapshots contaminated by noise arises in many imaging applications. Many techniques have been proposed for noise elimination that rely either on measurements of the autocorrelation and/or power spectrum of the observations, or on the assumption that the additive noise is white. The power spectrum is affected by additive noise, and in many situations, the noise is spatially or temporally correlated. The above techniques are sensitive to deviations from assumptions. The bispectrum is identically zero for random processes with symmetric distributions regardless of spatial or temporal correlations. This property along with its ability to retain phase and magnitude information, have led researchers to propose bispectral techniques for estimating parameters of random signals in noise. The bispectrum is also insensitive to translational motion. If these properties are to be taken advantage of to solve the moving object (deterministic signal in noise) reconstruction problem, it is necessary to obtain good estimates of the bispectrum of the object from the noisy observations. In order to do this it is necessary to restrict bispectrum estimation to a certain region of the frequency plane. A consequence of this is that several techniques proposed for bispectral analysis of random signals cannot be used. The paper develops new approaches which enable signal reconstruction from bispectrum measurements made over the restricted region. Simulations of application of these techniques to moving object reconstruction, data transmission over channels with jitter and noise, and image restoration, show that they are more robust with respect to the statistics of the contaminating noise than methods based on autocorrelation.