Abstract not found

This paper is concerned with regression under a "sum" of partial order constraints. Examples include locally monotonic, piecewise monotonic, runlength constrained, and unimodal and oligomodal regression. These are of interest not only in nonlinear filtering but also in density estimation and chromatographic analysis. It is shown that under a least absolute error criterion, these problems can be transformed into appropriate finite problems, which can then be efficiently solved via dynamic programming techniques. Although the result does not carry over to least squares regression, hybrid programming algorithms can be developed to solve least squares counterparts of certain problems in the class. Index Terms--- Dynamic programming, locally monotonic, monotone regression, nonlinear filtering, oligomodal, piecewise monotonic, regression under order constraints, runlength constrained, unimodal. I.

Compaction by optimal permutation (COPERM) is a tool for transform-domain energy compaction of broadband signals, whose foundation is a simple but powerful idea: Any signal can be transformed to resemble a more desirable (e.g., from a transform-domain compaction viewpoint) signal from a class of "target" signals (e.g., DCT basis functions) by means of a suitable permutation of its samples. One application of transformdomain energy compaction is in lossy compression. We pursue one possible thread in detail and demonstrate some interesting broadband image compression results. Index Terms---Broadband image compression, coding, energy compaction, permutation, textures. I.