This thesis studies the multiple description vector quantization with lattice codebooks (MDLVQ). The design of index assignment is crucial to the performance of MDLVQ. However, to our best knowledge, none of previous index assignment algorithms for MDLVQ is optimal. In this thesis, we propose a simple linear-time index assignment algorithm for MDLVQ with any K ≥ 2 balanced descriptions. We prov...

A fuzzy approach to color quantization and its application in content-based image indexing and retrieval is presented in this paper. There are some drawbacks associated with traditional color histogram such as color quantization. The goal in this paper is to develop an effective method to overcome several limitations, which are related to traditional color histogram. In the proposed method, fuz...

In this paper, rstly, it is proved that, for a fuzzy vector space, the set of its fuzzy bases de ned by Shi and Huang, is equivalent to the family of its bases de ned by P. Lubczonok. Secondly, for two fuzzy vector spaces, it is proved that they are isomorphic if and only if they have the same fuzzy dimension, and if their fuzzy dimensions are equal, then their dimensions are the same, however,...

In this paper a novel type of gain-shape vector quantization (GSVQ) is presented, denoted as Logarithmic Cubic Vector Quantization (LCVQ). LCVQ is based on a decomposition of the vector to be quantized into a gain factor and a shape vector which is a normalized version of the input vector. Both components are quantized independently and transmitted to the decoder. Compared to other GSVQ approac...

This paper describes a technique to compress images based on vector quantization. The vector quantizer is designed to reduce both perceptual irrelevancy and mathematical redundancy. This is done without using transforms and entropic coding, which are normally used respectively prior and after quantization. Because of its structure, the vector quantizer can be implemented efficiently as a unifor...

This paper is the extension of two-stage vector quantization–(spherical) lattice vector quantization (VQ–(S)LVQ) recently introduced by Pan and Fischer [1]. First, according to high resolution quantization theory, generalized vector quantization–lattice vector quantization (G-VQ–LVQ) is formulated in order to release the constraint of the spherical boundary for the second-stage lattice vector q...

We present an approach for the supervised online learning of object representations based on a biologically motivated architecture of visual processing. We use the output of a recently developed topographical feature hierarchy to provide a view-based representation of threedimensional objects using a dynamical vector quantization approach. For a simple short-term object memory model we demonstr...

a‎s we know, developing mathematical models and numerical procedures that would appropriately treat and solve systems of linear equations where some of the system's parameters are proposed as fuzzy numbers is very important in fuzzy set theory. for this reason, many researchers have used various numerical methods to solve fuzzy linear systems. in this paper, we define the concepts of midpoint a...