Multi-scale vector quantization with reconstruction trees

Competitive Neural Trees for Vector Quantization

This paper presents a self-organizing neural architecture for vector quantization, called Competitive Neural Tree (CNeT). At the node level, the CNeT employs unsupervised competitive learning. The CNeT performs hierarchical clustering of the feature vectors presented to it as examples, while its growth is controlled by a splitting criterion. Because of the tree structure, the prototype in the C...

Parallel BTF Compression with Multi-Level Vector Quantization in OpenCL

Bidirectional Texture Function (BTF) as an effective visual fidelity representation of surface appearance is becoming more and more widely used. In this paper we report on contributions to BTF data compression for multi-level vector quantization. We describe novel decompositions that improve the compression ratio by 15% in comparison with the original method, without loss of visual quality. Fur...

Multi-Scale Singularity Trees (MSSTs)

Vector quantization and fuzzy ranks for image reconstruction

The problem of clustering is often addressed with techniques based on a Voronoi partition of the data space. Vector quantization is based on a similar principle, but it is a different technical problem. We analyze some approaches to the synthesis of a vector quantization codebook, and their similarities with corresponding clustering algorithms. We outline the role of fuzzy concepts in these alg...

Vector quantization with complexity costs

Vector quantization is a data compression method where a set of data points is encoded by a reduced set of reference vectors, the codebook. We discuss a vector quantiza-tion strategy which jointly optimizes distortion errors and the codebook complexity, thereby, determining the size of the codebook. A maximum entropy estimation of the cost function yields an optimal number of reference vectors,...