Conclu.sitm We proposed a simple dynamic index mapping which considerably reduces the index entropy by expoiting interhlock correlation. It was shown that it provides a considerable amount of hit-rate reduction not only in ordinary VQ but also in many hybrid VQ systems. Since the proposed index encoding scheme requires only a small amount of additional computation and does not change the quanti...

In this paper, we examined three vector quantization (VQ) methods used for the unsupervised classification (clustering) of functional magnetic resonance imaging (fMRI) data. Classification means that each brain volume element (voxel), according to a given scanning raster, was assigned to one group of voxels based on similarity of the fMRI signal patterns. It was investigated how the VQ methods ...

Let’s begin by briefly reviewing the Matlab routines required for VQ-based image compression. First, note that the VQ parameters are set on lines 13-19 of prob1.m. The general VQ framework is implemented by learnVQ.m, which uses the subroutine kmeans.m to implement the k-means algorithm. (Note that, as recommended, we use findnn.m and lpnorm.m within the k-means implementation to allow flexible...

This paper deals with the construction and optimization of a hybrid speech recognition system that consists of a combination of a neural vector quantizer (VQ) and discrete HMMs. In our investigations an integration of VQ based classi cation in the continuous classi er framework is given and some constraints are derived that must hold for the pdfs in the discrete pattern classi er context. Furth...

Absrract -Vector quantization (VQ) has emerged as a viable approach for coding speech and image data in the last decade. Various vector quantizers are designed and their performance evaluated during the last few years. The hardware realizations of VQ encoder systems have been designed mainly for real-time speech coding ranging from the use of “off-the-shelf” components to applying VLSI technolo...

WRKY proteins are a class of transcription factors (TFs) involved in the regulation of various physiological processes, including the plant response to biotic and abiotic stresses. Recent studies in Arabidopsis have revealed that some WRKY TFs interact with a class of proteins designed as VQ proteins because of their typical conserved motif (FxxxVQxLTG). So far, no information is available abou...

This paper presents a Lossy image compression technique which is combination of discrete wavelet transform (DWT), Thresholding, Vector Quantization (VQ) and Huffman coding. Proposed method is as follows, First, DWT is performed on the original image then Globle Thresholding technique is applied and resulting coefficients are then vector quantized. VQ indices are Huffman coded to increase the co...

BACKGROUND Ventilation-perfusion (VQ) scanning is used when pulmonary embolism (PE) is suspected during pregnancy; however, the distribution of lung scan results and safety of VQ scanning have never been studied. OBJECTIVE To study the distribution of lung scan results and safety of VQ scanning as well as the safety of withholding anticoagulation therapy following a normal or nondiagnostic sc...

This letter proposes a method to design a vector quantizer (VQ) for robust performance under noisy channel conditions. By re-optimizing the quantizer at progressively lower levels of assumed channel noise, the design is less susceptible to poor local optima. The method is applied to: 1) channel-optimized VQ design; and 2) index assignment for a source-optimized VQ. For both problems, we demonst...

The paper presents a complete discrete statistical framework, based on a novel vector quantization (VQ) front-end process. This new VQ approach performs an optimal distribution of VQ codebook components on HMM states. This technique that we named the distributed vector quantization (DVQ) of hidden Markov models, succeeds in unifying acoustic micro-structure and phonetic macro-structure, when th...