Embedded Trellis Coded Quantization (E-TCQ) is introduced as an embedded quantization technique which achieves good rate-distortion performance for reasonable computational complexity. The performance of E-TCQ is investigated for memoryless Gaussian, Laplacian, and uniform sources. E-TCQ is shown to outperform multi-stage TCQ. For Gaussian and Laplacian sources the performance of E-TCQ shows la...

Quantized frame expansions are overcomplete representations of signals approximated by some quantizer. This scheme proved to be a very useful tool to combat quantization noise and erasures. Normally, a scalar quantizer is used in the quantization phase. In this paper we investigate the use of permutation codes, a beautiful source coding technique, as a quantizer. We explore consistent reconstru...

In rate-distortion theory it has been shown that vector quantization (VQ) outperforms its counterpart, scalar quantization (SQ), consistently [Shannon, 1959], [GRAY, 1998]. However the major problems that may prevent its wide acceptance in image compression applications is the computational complexity. The VQ compression system is highly asymmetry in that significantly more computing time is re...

In this paper, we address the problem of sparse signal recovery from scalar quantized compressed sensing measurements, via optimization. To compensate for compression losses due to dimensionality reduction and quantization, we consider a cost function that is more sparsity-inducing than the commonly used `1-norm. Besides, we enforce a quantization consistency constraint that naturally handles t...

The power spectrum of scalar field and space-time metric perturbations produced in the process of inflation of universe, have been presented in this paper by an alternative approach to field quantization namely, Krein space quantization [1, 2]. Auxiliary negative norm states, the modes of which do not interact with the physical world, have been utilized in this method. Presence of negative norm...

Improvement of the compression efficiency by modification of standard wavelet compression scheme with uniform scalar quantization and entropy coding of wavelet coefficients is considered. Generally Antonini filters ocurred the most effective for medical image compression. Quantization procedure is based on variable step size and adaptive threshold data selection. Quantized values coding concept...

This paper describes an adaptive-offset quantization scheme and considers its application to the lossy compression of grayscale document images. The technique involves scalar-quantizing and entropy-coding pixels sequentially, such that the quantizer’s offset is always chosen to minimize the expected number of bits emitted for each pixel, where the expectation is based on the predictive distribu...

Scalar quantizers with the minimum mean square error are designed in the case where the input signal and the quantization noise are uncorrelated. The probability density function (pdf) of the stochastic signal is assumed to be known. This design is then generalized to vector quantizers in the case where the expected value of the inner product between the input and quantization noise vector are ...

We introduce an image coding algorithm based on the cortex transform, matching pursuit and perceptual pruning that selects and codes only the most perceptually relevant image components, as determined by a new image indistinguishability criterion that derives from a maximum likelihood paradigm. We demonstrate that this method selects image components that produce images with higher subjective q...