Cascade Coding With Error-Constrained Relative Entropy Decoding

This work develops iterative algorithms for decoding cascade-coded images by Relative Entropy (RE) minimization. In cascade coding, blocks ofanimage are firsttransform-coded and then theretained coefficients are transmitted by using moment-preserving Block Truncation Coding (BTC). The BTC coding introduces a quantization error in the values of the retained coefficients. Upon reception, the distorted coefficients are used in reconstructing the image by the inverse transform, with the unretained coefficients set equal to zero. The proposed algorithms reconstruct the original image from the distorted coefficients by minimizing the RE of the image, with the coefficients used as constraints. In addition, the error introduced by the BTC coding is used as an additional constraint, since it is known to the receiver by the nature of the BTC coding. The iterative nature of the algorithm pertains to the way the algorithm uses the constraints, i.e. one at a time, with each reconstruction used as a prior for the next RE minimization. This is the first time that RE minimization with errors in the constraints has been used in image decompression even though it is common in spectrum estimation when there are errors in the correlation measurements

Reconstruction Of Transform-Coded Images By Entropy Methods

A new, universal approach to reconstructing transform-coded images is proposed. The method views the images as a probability mass function (pmf), allowing the retained coefficients of a transform (Karhunen-Loeve, discrete cosine, slant, etc.) to be thought of as averages of the basis functions over the pmf. This sets the stage for reconstructing the original images by using the maximum entropy principle (MEP) and the minimum relative entropy principle (MREP) with the retained coefficients as constraints in the extremizations. A formulation combining the two methods is also proposed, resulting in a reconstruction algorithm that is fast, proceeding in an iterative way using the estimate from each coefficient as a prior pmf for the next one via the MREP. The proposed approaches are illustrated with images compressed by discrete cosine transform coding, and the results are compared with standard reconstruction using the inverse discrete cosine transform

Overlapped Discrete Multitone Modulation For High Speed Copper Wire Communications

Multicarrier modulation possesses several properties which make it an attractive approach for high speed copper wire communications networks. Among these properties are the ability to efficiently access and distribute multiplexed data streams, and a reduced susceptibility to impulsive, as well as to narrowband channel disturbances. In digital implementations of multicarrier modulation, subcarrier generation and data modulation are accomplished digitally using orthogonal transformations of data blocks. These implementations are particularly efficient with regard to bandwidth utilization and transceiver complexity. In this paper, we present a form of digital multicarrier modulation which we refer to as overlapped discrete multitone, or discrete wavelet multitone (DWMT), modulation. For DWMT modulation, which is based on the application of M-band wavelet filters, the pulses for different data blocks overlap in time, and are designed to achieve a combination of subchannel spectral containm..