Slepian-Wolf Decoding Research Articles

Statistical Physics of Random Binning

We consider the model of random binning and finite-temperature decoding for Slepian-Wolf codes, from a statistical-mechanical perspective. While ordinary random channel coding is intimately related to the random energy model-a statistical-mechanical model of disordered magnetic materials, it turns out that random binning (for Slepian-Wolf coding) is analogous to another, related statistical-mechanical model of strong disorder, which we call the random dilution model. We use the latter analogy to characterize phase transitions pertaining to finite-temperature Slepian-Wolf decoding, which are somewhat similar, but not identical, to those of finite-temperature channel decoding. We then provide the exact random coding exponent of the bit error rate as a function of the coding rate and the decoding temperature, and discuss its properties. Finally, a few modifications and extensions of our results are outlined and discussed.

Erasure/List Exponents for Slepian–Wolf Decoding

We analyze random coding error exponents associated with erasure/list Slepian-Wolf decoding using two different methods and then compare the resulting bounds. The first method follows the well known techniques of Gallager and Forney and the second method is based on a technique of distance enumeration, or more generally, type class enumeration, which is rooted in the statistical mechanics of a disordered system that is related to the random energy model. The second method is guaranteed to yield exponent functions, which are at least as tight as those of the first method, and it is demonstrated that for certain combinations of coding rates and thresholds, the bounds of the second method are strictly tighter than those of the first method, by an arbitrarily large factor. The second method may even yield an infinite exponent at regions where the first method gives finite values.

Distributed near lossless compression algorithm for hyperspectral images

This paper presents a new near lossless compression algorithm for hyperspectral images based on distributed source coding. The algorithm is performed on blocks that have the same location and size in each band. Because the importance varies from block to block along the spectral orientation, an adaptive rate allocation algorithm that weights the energy of each block under the target rate constraints is introduced. A simple linear prediction model is employed to construct the side information of each block for Slepian–Wolf coding. The relationship between the quantized step size and the allocated rate of each block is determined under the condition of correct reconstruction with the side information at the Slepian–Wolf decoder. Slepian–Wolf coding is performed on the quantized version of each block. Experimental results show that the performance of the proposed algorithm is competitive with that of state-of-the-art compression algorithms, making it appropriate for on-board compression.

Encoder-driven rate control and mode decision for distributed video coding

To provide low-complexity encoding for video in unidirectional or offline compression scenarios, this paper proposes an efficient feedback-channel-free distributed video coding architecture featuring a novel encoder-driven rate control scheme in tandem with a designated mode selection process. To this end, the encoder features a novel low-complexity motion estimation technique to approximate the side-information (SI) available at the decoder. Then, a SI-dependent correlation channel estimation between the approximated SI and the original frames is used to derive the theoretically required rate for successful Slepian-Wolf (SW) decoding. Based on the evaluation of the expected trade-off between the estimated required coding rate and the estimated distortion outcome, a novel encoder-side mode decision module assigns a different coding mode to distinct portions of the coded frames. In this context, skip, intra and SW coding modes are supported. To reduce the effect of underestimation, the final SW rate is adjusted upwards using a novel rate formula. Additionally, a successive SI refinement technique is exploited at the decoder to decrease the number of SW decoding failures. Experimental results illustrate the benefit of the different coding options and show similar or superior compression performance with respect to the feedback-based DISCOVER benchmark system. Finally, the low-complexity encoding characteristics of the proposed system are confirmed, as well as the beneficial impact of the proposed scheme on the decoding complexity.

Near lossless compression of hyperspectral images based on distributed source coding

Effective compression technique of on-board hyperspectral images has been an active topic in the field of hyperspectral remote sensintg. In order to solve the effective compression of on-board hyperspectral images, a new distributed near lossless compression algorithm based on multilevel coset codes is proposed. Due to the diverse importance of each band, a new adaptive rate allocation algorithm is proposed, which allocates rational rate for each band according to the size of weight factor defined for hyperspectral images subject to the target rate constraints. Multiband prediction is introduced for Slepian-Wolf lossless coding and an optimal quantization algorithm is presented under the correct reconstruction of Slepian-Wolf decoder, which minimizes the distortion of reconstructed hyperspectral images under the target rate. Then Slepian-Wolf encoder exploits the correlation of the quantized values to generate the final bit streams. Experimental results show that the proposed algorithm has both higher compression efficiency and lower encoder complexity than several existing classical algorithms.

A practical scheduling and power/constellation allocation for three relay networks

Relays are being deployed in both fourth generation cellular systems and wide metropolitan area networks in order to increase coverage and spectral efficiency. In this article, we consider a network with three half-duplex relays assisting the transmission from a source towards a destination. By assuming that transmission time consists of a sequence of phases, we develop a transmission scheme in which spatial multiplexing is achieved by alternating the transmission of a single relay in odd phases and a couple of relays employing Slepian-Wolf encoding in even phases. Moreover, to simplify Slepian-Wolf decoding (consisting of a scheme with successive interference cancelation), we propose a suboptimal scheme in which cooperative relays employ quantized QAM constellations to yield at the destination the desired QAM constellation with no interference. We formalize the problem of maximizing network spectral efficiency optimizing (a) QAM size, (b) power allocation, (c) time allocation, and (d) relays transmitting and receiving at each phase. The performance of the proposed scheme is compared against existing techniques in typical wireless scenarios showing the merits of the proposed approach.

Robust Video Coding Based on Multiple Description Scalar Quantization With Side Information

This paper addresses the problem of video compression for robust transmission on lossy Internet networks. The approach developed relies on multiple description coding (MDC) principles. Predictive multiple description video coding has already been considered for robust video transmission over lossy channels. However, MDC, when combined with motion-compensated prediction, is known to suffer from predictive mismatch: in presence of losses, the prediction signal available at the decoder may differ from the one used at the encoder. In this paper, we describe a video compression scheme based on MDC and Wyner-Ziv (WZ) coding. The input sequence is structured into groups of pictures which contain one key frame and one WZ frame. Each frame is first transformed with a wavelet transform and the resulting frequency bands are quantized with a multiple description scalar quantizer. Two balanced descriptions of the video input are thus generated. The quantization indexes of the WZ frames are coded with an low-density parity-check accumulate-based Slepian-Wolf (SW) coder. The lateral receivers first decode the received key frame lateral descriptions, and then construct the side information needed for decoding the corresponding WZ frame descriptions by motion-compensated interpolation. In the case where the two WZ data descriptions are received, the central decoder can perform a separate SW decoding of the two sequences of quantization indexes. A joint iterative decoding approach of the two WZ descriptions is also described which improve the central WZ data decoding performance, however, at the expense of increased decoding complexity. The influence of the proposed iterative decoding technique and of the amount of redundancy on the lateral and central rate-distortion performance of the algorithm is studied.

Efficient Compression of Encrypted Grayscale Images

Lossless compression of encrypted sources can be achieved through Slepian-Wolf coding. For encrypted real-world sources, such as images, the key to improve the compression efficiency is how the source dependency is exploited. Approaches in the literature that make use of Markov properties in the Slepian-Wolf decoder do not work well for grayscale images. In this correspondence, we propose a resolution progressive compression scheme which compresses an encrypted image progressively in resolution, such that the decoder can observe a low-resolution version of the image, study local statistics based on it, and use the statistics to decode the next resolution level. Good performance is observed both theoretically and experimentally.