Near-capacity dirty-paper code design : a source-channel coding approach

This paper examines near-capacity dirty-paper code designs based on source-channel coding. We first point out that the performance loss in signal-to-noise ratio (SNR) in our code designs can be broken into the sum of the packing loss from channel coding and a modulo loss, which is a function of the granular loss from source coding and the target dirty-paper coding rate (or SNR). We then examine practical designs by combining trellis-coded quantization (TCQ) with both systematic and nonsystematic irregular repeat-accumulate (IRA) codes. Like previous approaches, we exploit the extrinsic information transfer (EXIT) chart technique for capacity-approaching IRA code design; but unlike previous approaches, we emphasize the role of strong source coding to achieve as much granular gain as possible using TCQ. Instead of systematic doping, we employ two relatively shifted TCQ codebooks, where the shift is optimized (via tuning the EXIT charts) to facilitate the IRA code design. Our designs synergistically combine TCQ with IRA codes so that they work together as well as they do individually. By bringing together TCQ (the best quantizer from the source coding community) and EXIT chart-based IRA code designs (the best from the channel coding community), we are able to approach the theoretical limit of dirty-paper coding. For example, at 0.25 bit per symbol (b/s), our best code design (with 2048-state TCQ) performs only 0.630 dB away from the Shannon capacity

Vector quantization

During the past ten years Vector Quantization (VQ) has developed from a theoretical possibility promised by Shannon's source coding theorems into a powerful and competitive technique for speech and image coding and compression at medium to low bit rates. In this survey, the basic ideas behind the design of vector quantizers are sketched and some comments made on the state-of-the-art and current research efforts

Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Nested turbo codes for the costa problem

Driven by applications in data-hiding, MIMO broadcast channel coding, precoding for interference cancellation, and transmitter cooperation in wireless networks, Costa coding has lately become a very active research area. In this paper, we first offer code design guidelines in terms of source- channel coding for algebraic binning. We then address practical code design based on nested lattice codes and propose nested turbo codes using turbo-like trellis-coded quantization (TCQ) for source coding and turbo trellis-coded modulation (TTCM) for channel coding. Compared to TCQ, turbo-like TCQ offers structural similarity between the source and channel coding components, leading to more efficient nesting with TTCM and better source coding performance. Due to the difference in effective dimensionality between turbo-like TCQ and TTCM, there is a performance tradeoff between these two components when they are nested together, meaning that the performance of turbo-like TCQ worsens as the TTCM code becomes stronger and vice versa. Optimization of this performance tradeoff leads to our code design that outperforms existing TCQ/TCM and TCQ/TTCM constructions and exhibits a gap of 0.94, 1.42 and 2.65 dB to the Costa capacity at 2.0, 1.0, and 0.5 bits/sample, respectively

A Study of trellis coded quantization for image compression

Trellis coded quantization has recently evolved as a powerful quantization technique in the world of lossy image compression. The aim of this thesis is to investigate the potential of trellis coded quantization in conjunction with two of the most popular image transforms today; the discrete cosine transform and the discrete wavelet trans form. Trellis coded quantization is compared with traditional scalar quantization. The 4-state and the 8-state trellis coded quantizers are compared in an attempt to come up with a quantifiable difference in their performances. The use of pdf-optimized quantizers for trellis coded quantization is also studied. Results for the simulations performed on two gray-scale images at an uncoded bit rate of 0.48 bits/pixel are presented by way of reconstructed images and the respective peak signal-to-noise ratios. It is evident from the results obtained that trellis coded quantization outperforms scalar quantization in both the discrete cosine transform and the discrete wavelet transform domains. The reconstructed images suggest that there does not seem to be any considerable gain in going from a 4-state to a 8-state trellis coded quantizer. Results also suggest that considerable gain can be had by employing pdf-optimized quantizers for trellis coded quantization instead of uniform quantizers

Wyner-Ziv coding based on TCQ and LDPC codes and extensions to multiterminal source coding

Driven by a host of emerging applications (e.g., sensor networks and wireless video), distributed source coding (i.e., Slepian-Wolf coding, Wyner-Ziv coding and various other forms of multiterminal source coding), has recently become a very active research area. In this thesis, we first design a practical coding scheme for the quadratic Gaussian Wyner-Ziv problem, because in this special case, no rate loss is suffered due to the unavailability of the side information at the encoder. In order to approach the Wyner-Ziv distortion limit D??W Z(R), the trellis coded quantization (TCQ) technique is employed to quantize the source X, and irregular LDPC code is used to implement Slepian-Wolf coding of the quantized source input Q(X) given the side information Y at the decoder. An optimal non-linear estimator is devised at the joint decoder to compute the conditional mean of the source X given the dequantized version of Q(X) and the side information Y . Assuming ideal Slepian-Wolf coding, our scheme performs only 0.2 dB away from the Wyner-Ziv limit D??W Z(R) at high rate, which mirrors the performance of entropy-coded TCQ in classic source coding. Practical designs perform 0.83 dB away from D??W Z(R) at medium rates. With 2-D trellis-coded vector quantization, the performance gap to D??W Z(R) is only 0.66 dB at 1.0 b/s and 0.47 dB at 3.3 b/s. We then extend the proposed Wyner-Ziv coding scheme to the quadratic Gaussian multiterminal source coding problem with two encoders. Both direct and indirect settings of multiterminal source coding are considered. An asymmetric code design containing one classical source coding component and one Wyner-Ziv coding component is first introduced and shown to be able to approach the corner points on the theoretically achievable limits in both settings. To approach any point on the theoretically achievable limits, a second approach based on source splitting is then described. One classical source coding component, two Wyner-Ziv coding components, and a linear estimator are employed in this design. Proofs are provided to show the achievability of any point on the theoretical limits in both settings by assuming that both the source coding and the Wyner-Ziv coding components are optimal. The performance of practical schemes is only 0.15 b/s away from the theoretical limits for the asymmetric approach, and up to 0.30 b/s away from the limits for the source splitting approach