This work presents a quantization technique for LSP parameters which results in a low bit-rate transmission while providing protection against channel errors. As a generalization of the so called Channel Optimized Vector Quantization (COVQ), Channel Optimized Matrix Quantization (COMQ) can remove intraframe and interframe LSP redundancy with the target of protecting the information sent through...

In this paper, we address the problem of quantizer design optimized for a source localization application in acoustic sensor networks where physically separated sensors make measurements of acoustic signal energy, quantize them, and transmit the quantized data to a fusion node, which then produces an estimate of the source location. We propose an iterative regular quantizer design algorithm tha...

Error di usion halftoning is a popular method of producing frequency modulated (FM) halftones. In FM halftoning the dot size and shape is xed (equal to one pixel) and the dot frequency is varied in accordance to the graylevel values of the underlying grayscale image. We generalize error di usion to produce FM halftones with user controlled dot size and shape using block quantization and a block...

This paper provides new error bounds on consistent reconstruction methods for signals observed from quantized random sensing. Those signal estimation techniques guarantee a perfect matching between the available quantized data and a reobservation of the estimated signal under the same sensing model. Focusing on dithered uniform scalar quantization of resolution δ > 0, we prove first that, given...

There have been a number of studies on sparse signal recovery from one-bit quantized measurements. Nevertheless, less attention has been paid to the choice of the quantization thresholds and its impact on the signal recovery performance. In this paper, we examine the problem of quantization in a general framework of one-bit compressed sensing with non-zero quantization thresholds. Our analysis ...

We propose a quantization-based numerical scheme for family of decoupled forward-backward stochastic differential equations. simplify the control in [1] so that our approach is fully based on recursive marginal quantization and does not involve any Monte Carlo simulation computation conditional expectations. analyse detail error provide some examples application to financial mathematics.

In this article, we develop a new approach to functional quantization, which consists in discretizing only a finite subset of the Karhunen-Loève coordinates of a continuous Gaussian semimartingale X. Using filtration enlargement techniques, we prove that the conditional distribution of X knowing its first Karhunen-Loève coordinates is a Gaussian semimartingale with respect to a bigger filtratio...

We introduce in this paper a new way of optimizing the natural extension of the quantization error using in k-means clustering to dissimilarity data. The proposed method is based on hierarchical clustering analysis combined with multi-level heuristic refinement. The method is computationally efficient and achieves better quantization errors than the relational k-means.