Maximum Entropy Reconstruction Of Moment Coded Images

The maximum entropy principle (MEP) is applied to the problem of reconstructing an image from knowledge of a finite set of its moments. This new approach is compared to the existing method of moments approach and is shown to have a clear edge in performance in all of the applications attempted. Compression ratios more than twice as high as those previously achieved are possible with the new MEP method

Technical Report: Region and Effect Inference for Safe Parallelism

In this paper, we present the first full regions-and-effects inference algorithm for explicitly parallel fork-join programs. We infer annotations inspired by Deterministic Parallel Java (DPJ) for a type-safe subset of C++. We chose the DPJ annotations because they give the \emph{strongest} safety guarantees of any existing concurrency-checking approach we know of, static or dynamic, and it is also the most expressive static checking system we know of that gives strong safety guarantees. This expressiveness, however, makes manual annotation difficult and tedious, which motivates the need for automatic inference, but it also makes the inference problem very challenging: the code may use region polymorphism, imperative updates with complex aliasing, arbitrary recursion, hierarchical region specifications, and wildcard elements to describe potentially infinite sets of regions. We express the inference as a constraint satisfaction problem and develop, implement, and evaluate an algorithm for solving it. The region and effect annotations inferred by the algorithm constitute a checkable proof of safe parallelism, and it can be recorded both for documentation and for fast and modular safety checking.Ope

Cascade Coding For Sub-Bit Image Compression

\u27Cascade coding,\u27 a technique of double coding an image is introduced in this paper. Blocks of the image are first transform-coded and the retained coefficients of the transform are then quantized by a Block Truncation Coding (BTC) algorithm for transmission or storage. Upon reception or recall, the quantized transform coefficients are used in the inverse transform to reconstruct the image. The new method combines the spatial correlation characteristics of the transform methods with the ease of implementation of the BTC. Illustrations presented here on sub-bit image coding, shows it to perform consistently better than straight Cosine Transform (DCT) coding

High-Fidelity Integrated Lossless/Lossy Compression And Reconstruction Of Images

The paper describes a compression scheme which is used in the reconstruction of images. An integrated lossless/lossy scheme is presented. A compression scheme presented aims at improving the fidelity of reconstructed images. An analysis is presented at the reasons for the modest performance of many lossless compression algorithms

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

Iterative Reconstruction Of Transform-Coded Images Using Relative Entropy.

A universal method of decoding transform-coded images using the principle of minimum relative entropy (MREP) is considered that was previously introduced by the authors. Here they examine the MREP\u27s iterative convergence properties by applying it to image data compressed by the discrete cosine transform and running the MREP iterative algorithm until it stabilizes

More on informational entropy, redundancy and sound change

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