previously published scalar quantization algorithm [3], This paper presents a novel error-free (infinite-precision) architecture for the fast implementation of both 2-D Discrete Cosine Transform and Inverse DCT. The architecture uses a new algebraic integer quantization of a 1-D radix-8 DCT that allows the separable computation of a 2D DCT without any intermediate number representation conversi...

This paper proposes some fast and simple quantization techniques to display.The main reason for adopting different techniques in vector quantizers VQ is to design an optimal quantizer. Since the actual probability distributions of image.Abstract: Image Compression is a technique for competently coding digital. Vector Quantization VQ is a block-coding technique that quantizes blocks of data.Defi...

The statistical properties of the error in uniform scalar quantization have been analyzed by a number of authors in the past, and is a well understood topic today. The analysis has also been extended to the case of dithered quantizers, and the advantages and limitations of dithering have been studied and well documented in the literature. Lattice vector quantization is a natural extension into ...

Using the Channel State Information (CSI) at the transmitter is fundamental for the precoder design in Multi-user Multiple Input Single Output (MU-MISO-OFDM) systems. In Frequency Division Duplex (FDD) systems, CSI can be just available at the transmitter through a limited feedback channel [1], where we assume that each user quantizes its channel direction with a finite number of quantization b...

Quantization is a necessary intermediate step in any digital processing device. The Memoryless Scalar quantization and the Sigma Delta quantization are two well known quantization schemes with different emphasis. In this talk, I will discuss the quantization problem raised in frame theory and compressed sensing, and explain the benefit that Sigma Delta quantization can deliver. In particular, w...