A new method is presented of cell loss protection for packet video environments that uses scrambling and deinterleaving in conjunction with block transform coding of the video signal. The input video sequence is first de-interleaved by separating the sequence into subsequences using a subsampling process. This allows better prediction of lost data when packets are lost. Each subsequence is then coded using the DCT, and the coded coefficients are packetized into cells in a pseudorandom, scrambled manner. Consequently, if several adjacent cells are lost, the loss of information will be spatially and spectrally random. Because of the random nature of the lost information, recovery from cell loss and concealment of the loss data by prediction from surrounding coefficients is greatly improved. The result is an improvement in perceptual quality over existing packetization methods when cells are lost.>

We present a new adaptive vector-quantization scheme with codebook replenishment for color image-sequence compression in this paper. This scheme uses mean/residual vector quantization with binary-tree search algorithm to vector-quantize the Y component and the low-passed I and Q components of color images. It also incorporates an objective criteria for codebook replenishment. The mean and label replenishment technique is also applied to further reduce the bit-rate. Computer simulations on two 60-frame, 512 × 512 color image-sequences have demonstrated the effectiveness of the proposed technique.

The constrained optimization problem for image restoration, utilizing incomplete information and partial constraints, is formulated using nonlinear programming techniques. This method restores a distorted image by optimizing a chosen object function subject to available constraints. The penalty function method of nonlinear programming is used. Both linear or nonlinear object function, and linear or nonlinear constraint functions can be incorporated in the formulation. This formulation provides a generalized approach to solve constrained optimization problems for image restoration. Experiments using this scheme have been performed. The results are compared with those obtained from other restoration methods and the comparative study is presented.

A constrained iterative image restoration method is applied to multichannel diffraction-limited imagery. This method is based on the Gerchberg-Papoulis algorithm utilizing incomplete information and partial constraints. The procedure is described using the orthogonal projection operators which project onto two prescribed subspaces iteratively. Its properties and limitations are presented. The effect of noise was investigated and a better understanding of the performance of the algorithm with noisy data has been achieved. The restoration scheme with the selection of appropriate constraints was applied to a practical problem. The 6.6, 10.7, 18, and 21 GHz satellite images obtained by the scanning multichannel microwave radiometer (SMMR), each having different spatial resolution, were restored to a common, high resolution (that of the 37 GHz channels) to demonstrate the effectiveness of the method. Both simulated data and real data were used in this study. The restored multichannel images may be utilized to retrieve rainfall distributions.

A minimization approach is presented for a modulo-2 expansion of a switching function. This method uses mixed polarity representation for the variables A local optimization procedure is presented which requires modest computation and is easily expanded. The procedure applied to the 32 DES select functions of six variables is reported.