A new form of trellis coded quantization is presented based on uniform quantization thresholds and "on-the-fly" codeword training. The universal trellis coded quantization (UTCQ) technique possesses several desirable features. Neither stored codebooks nor a codebook design algorithm are needed, yet UTCQ performance is comparable with fully optimized entropy-constrained trellis coded quantization (ECTCQ) for most rates. The UTCQ codebook and trellis geometry are symmetric with respect to trellis superset. For sources with a symmetric probability density, this allows use of a single variable-rate code in subsequent entropy coding. Uniform thresholds simplify the quantization process by eliminating binary tree searches. Performance of UTCQ for quantization of memoryless Gaussian and Laplacian sources is presented. This work was supported in part by SAIC and by the National Science Foundation under Grant No. 9258374. I INTRODUCTION TCQ has been shown to be an effective technique for q...

We study codes on graphs combined with an iterative message passing algorithm for quantization. Specifically, we consider the binary erasure quantization (BEQ) problem which is the dual of the binary erasure channel (BEC) coding problem. We show

Digital images have become an important source of information in the modern world of communication systems. In their raw form, digital images require a tremendous amount of memory. Many research efforts have been devoted to the problem of image compression in the last two decades. Two different compression categories must be distinguished: lossless and lossy. Lossless compression is achieved if no distortion is introduced in the coded image. Applications requiring this type of compression include medical imaging and satellite photography. For applications such as video--telephony or multimedia applications some loss of information is usually tolerated in exchange for a high compression ratio.

this article, we propose a new way of removing this redundancy in a completely distributed manner, i.e., without the sensors needing to talk to one another. Our constructive framework for this problem is dubbed DISCUS (distributed source coding using syndromes) and is inspired by fundamental concepts from information theory. In this article, we review the main ideas, provide illustrations, and give the intuition behind the theory that enables this framework

A new combined precoding/shaping technique for fast digital transmission over twisted pair lines is proposed. Major advantages of this "dynamics shaping" are: Dynamics of the signal at the input of the decision device are reduced by a great amount. Thereby, A/D-conversion, adaptive equalization, and symbol timing are rather facilitated. A trade-off between signal dynamics at the transmitter output, decision device input and SNR-gain by noise whitening is offered. For dynamics limitation relevant in practice, gains up to 6 dB are achieved. Additionally, the transmitter can be fixed to a typical application because, in contrast to Tomlinson-Harashima or other precoding techniques, blind adaptive equalization is practicable to remove residual intersymbol interference in the case of a mismatch of precoding and actual cable characteristics. The residual SNR-loss is negligible in most applications. SNR-gains due to noise prediction, channel coding and signal shaping simply can be combined us...

In this paper, we introduce a novel technique for adaptive scalar quantization. Adaptivity is useful in applications, including image compression, where the statistics of the source are either not known a priori or will change over time. Our algorithm uses previously quantized samples to estimate the distribution of the source, and does not require that side information be sent in order to adapt to changing source statistics. Our quantization scheme is thus backward adaptive. We propose that an adaptive quantizer can be separated into two building blocks, namely, model estimation and quantizer design. The model estimation produces an estimate of the changing source probability density function, which is then used to redesign the quantizer using standard techniques. We introduce nonparametric estimation techniques that only assume smoothness of the input distribution. We discuss the various sources of error in our estimation and argue that, for a wide class of sources with a smooth probability density function (pdf), we provide a good approximation to a "universal" quantizer, with the approximation becoming better as the rate increases. We study the performance of our scheme and show how the loss due to adaptivity is minimal in typical scenarios. In particular, we provide examples and show how our technique can achieve signalto -noise ratios (SNR's) within 0.05 dB of the optimal Lloyd--Max quantizer (LMQ) for a memoryless source, while achieving over 1.5 dB gain over a fixed quantizer for a bimodal source.

Abstract: We propose a new construction for low-density source codes with multiple parameters that can be tuned to optimize the performance of the code. In addition, we introduce a set of analysis techniques for deriving upper bounds for the expected distortion of our construction, as well as more general low-density constructions. We show that (with an optimal encoding algorithm) our codes achieve the rate-distortion bound for a binary symmetric source and Hamming distortion. Our methods also provide rigorous upper bounds on the minimum distortion achievable by previously proposed low-density constructions. 1

Abstract — We present a construction of multiple description trellis-coded quantizers. We use the tensor product of trellises to build a trellis which is applicable to multiple description coding. The problems of index assignment and set partitioning for the resulting trellis are considered. The Viterbi algorithm provides the best path for encoding and the design procedure utilizes a generalized Lloyd algorithm. The encoding process simultaneously generates all the transmitted sequences. Furthermore, the complexity of the scheme is almost independent of the rate. The quantizer provides remarkable performance with little encoding complexity. Index Terms — Diversity systems, multiple description, source coding, trellis-coded quantization.

Contents 6.1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 161 6.2 Adaptation algorithm : : : : : : : : : : : : : : : : : : : : : : : : : : : 167 6.3 Convergence of the adaptive quantizer : : : : : : : : : : : : : : : : : 179 6.4 Experimental results : : : : : : : : : : : : : : : : : : : : : : : : : : : 186 6.5 Conclusions and future work : : : : : : : : : : : : : : : : : : : : : : : 190 6.1 Introduction The most successful methods for lossless compression of data, such as arithmetic coding [55, 116, 80], Lempel-Ziv coding [120] or dynamic Huffman coding [35, 54, 111], are all adaptive (see [6] for an extensive review of lossless compression). While the initial work on entropy coding (e.g. Huffman coding) relied on knowing, or measuring, the source distribution, adaptive schemes make no prior assumptions on the source statistics, which the coders try to l

In this paper, we present coding techniques that enable progressive transmission when trellis coded quantization (TCQ) is applied to wavelet coefficients. A method for approximately inverting TCQ in the absence of least signficant bits is developed. Results are presented using different rate allocation strategies (optimal rate allocation and constant quantization step size) and different entropy coders (the SPIHT entropy coder and a bit plane coder). The wavelet-TCQ coder using optimal bit allocation and a progressively decodable bit plane coder yields excellent coding efficiency while supporting progressive modes analogous to those available in JPEG. EDICS: IP1.1 Coding Permission to publish this abstract separately is granted. This work was supported in part by the National Science Foundation under Grant No. NCR9258374. y A. Bilgin and M. W. Marcellin are with the Department of Electrical and Computer Engineering, The University of Arizona, Tucson, AZ 85721. P. J. Sementilli is ...