A novel variable-rate vector quantizer (VQ) design algorithm using both fuzzy and competitive learning technique is presented. The algorithm enjoys better rate-distortion performance than that of other existing fuzzy clustering and competitive learning algorithms. In addition, the learning algorithm is less sensitive to the selection of initial reproduction vectors. Therefore, the algorithm can...

Storaging or transmission of speech signals at very low bit rate is a hot area in the field of speech processing. We used stochastic inter-frame interpolators and vector quantization (VQ) as a new method for developing a high quality 1200 BPS speech vocoder. The objective and subjecgtive test results show that performance of the new vocoder is compairable with 4800 BPS standard vocoders (as CELP).

The task of our research is to form phone-like models and a phoneme-like set from spoken word samples without using any transcriptions except for the lexical identi cation of each word in a vocabulary. This framework is derived from two motivations: 1) automatic design of optimal speech recognition units and structures of phone models, and 2) multi-lingual speech recognition based on languagein...

Digital watermarking technique has been presented and widely researched to solve some important issues in the digital world, such as copyright protection, copy protection and content authentication. Several robust watermarking schemes based on vector quantization (VQ) have been presented. In this paper, we present a new digital image watermarking method based on SOFM vector quantizer for color ...

Vector Quantization (VQ) is one of the lossy image compression techniques. VQ comprises of three different phases: Codebook Generation, Image Encoding and Image Decoding. The performance of VQ is mainly based on the codebook generation phase. In this paper, five different codebook generation techniques namely the Simple Codebook Generation (SCG), Ordered Codebook Generation (OCG), Codebook Gene...

We address the problem of blur identification and image selection with statistical blur priors in the context of the vector quantization (VQ) based framework. Firstly, we assume some dominant blur priors for estimating point spread functions (PSFs) of blurred frames in Bayesian MAP estimation. The blurred frames with estimated PSFs can be stored in VQ-based multiple codebooks. These codebooks c...

In this paper a stochastic generalisation of the standard Linde-BuzoGray (LBG) approach to vector quantiser (VQ) design is presented, in which the encoder is implemented as the sampling of a vector of code indices from a probability distribution derived from the input vector, and the decoder is implemented as a superposition of reconstruction vectors, and the stochastic VQ is optimised using a ...

We present a combination of an extended vector quantization (VQ) algorithm for training a speaker model and a gaussian interpretation of the VQ speaker model in the veri cation phase. This leads to a large decrease of the error rates compared to normal vector quantization and only a slight deterioration compared to full Gaussian mixture model (GMM) training. The training costs of the new method...

We give an explicit description of the Fqi-rational points on the Fermat curve uq−1+vq−1+wq−1 = 0, for i ∈ {1, 2, 3}. As a consequence, we observe that for any such point (u, v, w), the product uvw is a cube in Fqi . We also describe the Fq2-rational points on the Fermat surface uq−1 + vq−1 + wq−1 + xq−1 = 0.