Some past work has proposed to use lossy compression to remove noise, based on the rationale that a reasonable compression method retains the dominant signal features more than the randomness of the noise. Building on this theme, we explain why compression (via coefficient quantization

A practical suboptimal (variable source coding) algorithm for lossy data compression is presented. This scheme is based on approximate string matching, and it naturally extends the lossless Lempel-Ziv data compression scheme. Among others we consider the typical length of approximately repeated

types of lossy links, and show that the answer depends on the maximum number of processes that may crash and the nature of the problem to be solved. In particular, we prove that the answer is positive if fewer than half of the processes may crash or if the problem speci®cation does not refer

ii The theory of high rate lossy source coding is well developed both with respect to the practice of quantization and its fundamental rate distortion limits. But many modern compression systems for natural signals such as audio and images operate at lower rates, which are not covered

This paper introduces a simple and efficient representation for natural images. We partition an image into blocks and treat the blocks as vectors in a high-dimensional space. We then fit a piece-wise linear model (i.e. a union of affine subspaces) to the vectors at each down-sampling scale. We call

Abstract-The scattered fields of a number of targets in free space are measured. Their complex natural resonances are extracted from the late time responses, using the generalized pencil-of-function method. The complex natural resonances, as the targets are immersed in a lossy medium

Language models are probability distributions over a set of unilingual natural language text used in many natural language processing tasks such as statistical machine trans-lation, information retrieval, and speech processing. Since more well-formed training data means a better model

Abstract — We study the problem of secure lossy source coding with side information. In all works on this problem, either the equivocation of the source at the eavesdropper or the equivocation of the legitimate user’s reconstruction of the source at the eavesdropper is used as the measure

Abstract—The three-node multiterminal lossy source coding problem is investigated. We derive an inner bound to the general rate-distortion region of this problem which appears to be the natural extension of the seminal work by Kaspi [1] on the interactive two-terminal source coding problem

that pixels are independent. This is done for the case where the data is postulated to live in an affine subspaces of the embedding space of the raw data. We apply our scheme to learn sparse bases for natural images that discount domain deformations and hence significantly decrease the complexity of codebooks