Angelos D. Liveris

Distributed source coding for sensor networks

In recent years, sensor research has been undergoing a quiet revolution, promising to have a significant impact throughout society that could quite possibly dwarf previous milestones in the information revolution. Realizing the great promise of sensor networks requires more than a mere advance in individual technologies. It relies on many components working together in an efficient, unattended, comprehensible, and trustworthy manner. One of the enabling technologies in sensor networks is the distributed source coding (DSC), which refers to the compression of the multiple correlated sensor outputs that does not communicate with each other. DSC allows a many-to-one video coding paradigm that effectively swaps encoder-decoder complexity with respect to conventional video coding, thereby representing a fundamental concept shift in video processing. This article has presented an intensive discussion on two DSC techniques, namely Slepian-Wolf coding and Wyner-Ziv coding. The Slepian and Wolf coding have theoretically shown that separate encoding is as efficient as joint coding for lossless compression in channel coding.

Compression of binary sources with side information at the decoder using LDPC codes

We show how low-density parity-check (LDPC) codes can be used to compress close to the Slepian-Wolf limit for correlated binary sources. Focusing on the asymmetric case of compression of an equiprobable memoryless binary source with side information at the decoder, the approach is based on viewing the correlation as a channel and applying the syndrome concept. The encoding and decoding procedures are explained in detail. The performance achieved is seen to be better than recently published results using turbo codes and very close to the Slepian-Wolf limit.

Exploiting faster-than-Nyquist signaling

Faster-than-Nyquist signaling introduces intersymbol interference, but increases the bit rate while preserving the signaling bandwidth. For sinc pulses, it has been established that with a small increase in the signaling rate beyond the Nyquist rate, there is no reduction in the minimum Euclidean distance for binary signaling. We generalize these observations to the family of raised-cosine pulses. The structure of the error events that reduce the minimum distance is examined, and constrained coding ideas are suggested that theoretically allow even faster signaling. Then we propose ways of achieving these gains practically by designing appropriate constrained codes and through equalization and iterative joint equalization and decoding (turbo equalization).

On code design for the Slepian-Wolf problem and lossless multiterminal networks

A Slepian-Wolf coding scheme for compressing two uniform memoryless binary sources using a single channel code that can achieve arbitrary rate allocation among encoders was outlined in the work of Pradhan and Ramchandran. Inspired by this work, we address the problem of practical code design for general multiterminal lossless networks where multiple memoryless correlated binary sources are separately compressed and sent; each decoder receives a set of compressed sources and attempts to jointly reconstruct them. First, we propose a near-lossless practical code design for the Slepian-Wolf system with multiple sources. For two uniform sources, if the code approaches the capacity of the channel that models the correlation between the sources, then the system will approach the theoretical limit. Thus, the great advantage of this design method is its possibility to approach the theoretical limits with a single channel code for any rate allocation among the encoders. Based on Slepian-Wolf code constructions, we continue with providing practical designs for the general lossless multiterminal network which consists of an arbitrary number of encoders and decoders. Using irregular repeat-accumulate and turbo codes in our designs, we obtain the best results reported so far and almost reach the theoretical bounds.

Joint source-channel coding of binary sources with side information at the decoder using IRA codes

We use systematic irregular repeat accumulate (IRA) codes as source-channel codes for the transmission of an equiprobable memoryless binary source with side information at the decoder. A special case of this problem is joint source-channel coding for a nonequiprobable memoryless binary source. The theoretical limits of this problem are given by combining the Slepian-Wolf theorem, the source entropy in the special case, with the channel capacity. The approach is based on viewing the correlation between the binary source output and the side information as a separate channel or an enhancement of the original channel. The joint source-channel encoding, decoding and code design procedures are explained in detail. The simulated performance results are better than the recently published solutions using turbo codes and very close to the theoretical limit.

Design of Slepian-Wolf codes by channel code partitioning

A Slepian-Wolf coding scheme that can achieve arbitrary rate allocation among two encoders was outlined in the work of Pradhan and Ramchandran. Inspired by this work, we start with a detailed solution for general (asymmetric or symmetric) Slepian-Wolf coding based on partitioning a single systematic channel code, and continue with practical code designs using advanced channel codes. By using systematic IRA and turbo codes, we devise a powerful scheme that is capable of approaching any point on the Slepian-Wolf bound. We further study an extension of the technique to multiple sources, and show that for a particular correlation model among the sources, a single practical channel code can be designed for coding all the sources in symmetric and asymmetric scenarios. If the code approaches the capacity of the channel that models the correlation between the sources, then the system will approach the Slepian-Wolf limit. Using systematic IRA and punctured turbo codes for coding two binary sources, each being independent identically distributed, with correlation modeled by a binary symmetric channel, we obtain results which are 0.04 bits away from the theoretical limit in both symmetric and asymmetric Slepian-Wolf settings.

A distributed source coding technique for correlated images using turbo-codes

The Slepian-Wolf (1973) theorem states that the output of two correlated sources can be compressed to the same extent without loss, whether they communicate with each other or not, provided that decompression takes place at a joint decoder. We present a distributed source coding scheme for correlated images which uses modulo encoding of pixel values and encoding (compression) of the resulting symbols with binary and nonbinary turbo-codes, so that larger rate savings than using modulo encoding. alone are achieved, practically without loss.

Distributed compression of binary sources using conventional parallel and serial concatenated convolutional codes

It is shown how conventional parallel (turbo) and serial concatenated convolutional codes can be used to compress close to the Slepian-Wolf limit for the correlated binary sources. Conventional refers to codes already used in channel coding. Focusing on the asymmetric case of compression of an equipolarable memoryless binary source with side information at the decoder, the approach is based on modeling the correlation as a channel and using syndromes. The encoding and decoding procedures are explained in detail. The performance achieved is seen to be better than the recently published results using nonconventional turbo codes and close to the Slepian-Wolf limit.

Slepian-Wolf Coded Nested Lattice Quantization for Wyner-Ziv Coding: High-Rate Performance Analysis and Code Design

Nested lattice quantization provides a practical scheme for Wyner-Ziv coding. This paper examines the high-rate performance of nested lattice quantizers and gives the theoretical performance for general continuous sources. In the quadratic Gaussian case, as the rate increases, we observe an increasing gap between the performance of finite-dimensional nested lattice quantizers and the Wyner-Ziv distortion-rate function. We argue that this is because the boundary gain decreases as the rate of the nested lattice quantizers increases. To increase the boundary gain and ultimately boost the overall performance, a new practical Wyner-Ziv coding scheme called Slepian-Wolf coded nested lattice quantization (SWC-NQ) is proposed, where Slepian-Wolf coding is applied to the quantization indices of the source for the purpose of compression with side information at the decoder. Theoretical analysis shows that for the quadratic Gaussian case and at high rate, SWC-NQ performs the same as conventional entropy-coded lattice quantization with the side information available at both the encoder and the decoder. Furthermore, a nonlinear minimum mean-square error (MSE) estimator is introduced at the decoder, which is theoretically proven to degenerate to the linear minimum MSE estimator at high rate and experimentally shown to outperform the linear estimator at low rate. Practical designs of one- and two-dimensional nested lattice quantizers together with multilevel low-density parity-check (LDPC) codes for Slepian-Wolf coding give performance close to the theoretical limits of SWC-NQ

Slepian-Wolf coded nested quantization (SWC-NQ) for Wyner-Ziv coding: performance analysis and code design

This paper examines the high-rate performance of low-dimensional nested lattice quantizers for the quadratic Gaussian Wyner-Ziv problem, using a pair of nested lattices with the same dimensionality. As the rate increases, the gap increases between the performances of low dimensional nested lattice quantizers and the Wyner-Ziv rate-distortion function. This gap is due to the relatively weak channel coding component (or coarse lattice) in the nested lattice pair. To enhance the lattice channel code and boost the overall performance, Slepian-Wolf coding is applied to the quantization indices to achieve further compression. Thereby a Wyner-Ziv coding paradigm is introduced using Slepian-Wolf coded nested lattice quantization (SWC-NQ). Theoretical analysis and simulation results show that, for the quadratic Gaussian source and at high rate, SWC-NQ performs the same as traditional entropy-constrained lattice quantization with side information available at both the encoder and decoder.