|An improved wavelet compression algorithm for ECG signals has been developed with the use of vector quantization on wavelet coeecients. Vector quantization on scales of long duration and low dynamic range retains feature integrity of the ECG with a very low bit-per-sample rate. Preliminary results indicate that the proposed method excels over standard techniques for high delity compression .

We propose a new learning method, "Generalized Learning Vector Quantization (GLVQ)," in which reference vectors are updated based on the steepest descent method in order to minimize the cost function . The cost function is determined so that the obtained learning rule satisfies the convergence condition. We prove that Kohonen's rule as used in LVQ does not satisfy the convergence condition and ...

Gauss mixtures are a popular class of models in statistics and statistical signal processing because they can provide good £ts to smooth densities, because they have a rich theory, and because the can be well estimated by existing algorithms such as the EM algorithm. We here extend an information theortic extremal property for source coding from Gaussian sources to Gauss mixtures using high rat...

The block processing inherent in the use of traditional vector quantization (VQ) schemes typically gives rise to perceptually distracting blocking artifacts. We demonstrate that such artifacts can, in general, be virtually eliminated via an e cient lapped VQ strategy. With lapped VQ schemes, blocks are obtained from the source in an overlapped manner, and reconstructed via superposition of over...

In this paper, a novel scheme for vector quantization (VQ) is proposed. A file called visible index file is used to record the coding result. The decompressed image reconstructed from the visible index file is the same as the one recovered using traditional VQ index file; however, the visible index file looks like the original image, and is therefore more convenient for the management of index ...

An entropy-constrained residual vector quantization design algorithm is used to design codebooks for image coding. Entropy-constrained residual vector quantization has several important advantages. It can outperform entropy-constrained vector quantization in terms of rate-distortion performance, memory, and computation requirements. It can also be used to design vector quantizers with relativel...

We propose a model that can learn parts-based representations of highdimensional data. Our key assumption is that the dimensions of the data can be separated into several disjoint subsets, or factors, which take on values independently of each other. We assume each factor has a small number of discrete states, and model it using a vector quantizer. The selected states of each factor represent t...

Natural images can be segmented into regions with widely varying perceptual importance. There are three types of regions in a typical image. The region in which the contrast between objects and background is high is categorized as the edge. Due to the high contrast, human eyes will naturally pay much attention to the edges. Thus, edge regions are very important for human perception. The second ...

In this paper, we present a new multiple stage vector quantization method that allows the adaptation of the quantizer to signal to be coded. This adaptation is computationally very simple and is made with no increase in bit-rate. The resul t ing quant izer prov ides a robust performance across different speakers and environments. I t has been applied to the quantization of the LPC parameters an...

Vector quantization methods are confronted with a model selection problem, namely the number of prototypical feature representatives to model each class. In this paper we present an incremental learning scheme in the context of figure-ground segmentation. In presence of local adaptive metrics and supervised noisy information we use a parallel evaluation scheme combined with a local utility func...