This paper presents a multiresolution adaptive vector quantization and perceptual based multiview image coding scheme. It decorrelates the input views into a number of subbands using a lifting based wavelet transform. The coefficients in the same subbands of different views are divided into vectors and then joined together. The resulting vectors are then vector quantized using an adaptive vecto...

This paper presents a novel vector quantization (VQ) technique in which the quantized vector is formed by adding the transformed outputs of a multistage codebook rather than just adding the outputs of the stages as in regular multistage vector quantization (MSVQ). The transformations are selected from a family of linear transformations represented by a codebook of matrices. This technique can b...

In this paper, a new technique for lossy compression of halftone images is proposed based on the vector quantization technique. A conventional vector quantization encoder is modified such that it embeds a block-based error diffusion process and takes a HVS model into account during the compression. This modification significantly improves the visual performance of encoded images while the compr...

This paper proposes new method for designing scalar quantizers. The proposed method, denoted as hybrid method, combines two quantization techniques, the companding technique and the Lloyd-Max's algorithm. In this paper an exact and complete analysis of the hybrid quantization method considering the Laplacian input signals is carried out. Furthermore, two approaches to the problem of finding the...

An improved video subband coding technique is presented. It is based on a spatially and spectrally localizing subband analysis, followed by scalar quantization and direct arithmetic coding. The quantization and coding parameters are adapted on the basis of local energy of the subband pixels. The proposed technique automatically achieves near-optimal rate allocation with respect to mean-square e...

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...

An efficient algorithm for color image quantization is proposed based on a new vector quantization technique that we call sequential scalar quantization. The scalar components of the 3-D color vector are individually quantized in a predetermined sequence. With this technique, the color palette is designed very efficiently, while pixel mapping is performed with no computation. To obtain an optim...

Color Image quantization (CQ) is an important problem in computer graphics, image and processing. The aim of quantization is to reduce colors in an image with minimum distortion. Clustering is a widely used technique for color quantization; all colors in an image are grouped to small clusters. In this paper, we proposed a new hybrid approach for color quantization using firefly algorithm (FA) a...

We introduce a technique for dimensionality estimation based on the notion of quantization dimension, which connects the asymptotic optimal quantization error for a probability distribution on a manifold to its intrinsic dimension. The definition of quantization dimension yields a family of estimation algorithms, whose limiting case is equivalent to a recent method based on packing numbers. Usi...