Wavelet Thresholding via MDL: Simultaneous Denoising and Compression


In the context of wavelet denoising and compression, we study minimum description length (MDL) criteria for model selection criteria as flexible forms of thresholding. Mixture MDL methods based on a single Laplacian, a two-piece Laplacian, and a generalized Gaussian prior are shown to be adaptive thresholding rules. While achieving mean squared error performance comparable with other popular thresholding schemes, the MDL procedures tend to keep far fewer coefficients. From this property, we demonstrate that our methods represent excellent tools for simultaneous denoising and compression. We make this claim precise by analyzing MDL thresholding in two optimality frameworks; one in which we measure rate and distortion based on quantized coefficients and one in which we do not quantize, but instead record rate simply as the number of non-zero coefficients

Similar works

Full text

oaioai:CiteSeerX.psu: time updated on 10/22/2014

This paper was published in CiteSeerX.

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.