Central Decoder Research Articles

Rate Region of the One-Help-Two Quadratic Gaussian Source-Coding Problem With Markovity

We study the quadratic Gaussian one-help-two source-coding problem with Markovity, in which three encoders separately encode the components of a memoryless vector-Gaussian source that form a Markov chain and the central decoder aims to reproduce the first and the second components in the chain subject to individual mean-squared distortion constraints. We determine the rate region under a high-resolution assumption for the middle source.

On Distributed Learning With Constant Communication Bits

In this paper, we study a distributed learning problem constrained by constant communication bits. Specifically, we consider the distributed hypothesis testing (DHT) problem where two distributed nodes are constrained to transmit a constant number of bits to a central decoder. In such settings, we show that in order to achieve the optimal error exponents, it suffices to consider the empirical distributions of observed data sequences and encode them to the transmission bits. With such a coding strategy, we develop a geometric approach in the distribution spaces and establish an inner bound of error exponent regions. In particular, we show the optimal achievable error exponents and coding schemes for the following cases: (i) both nodes can transmit <inline-formula> <tex-math notation="LaTeX">$\log _{2}3$ </tex-math></inline-formula> bits; (ii) one of the nodes can transmit 1 bit, and the other node is not constrained; (iii) the joint distribution of the nodes are conditionally independent given one hypothesis. Furthermore, we provide several numerical examples for illustrating the theoretical results. Our results provide theoretical guidance for designing practical distributed learning rules, and the developed approach also reveals new potentials for establishing error exponents for DHT with more general communication constraints.

Precoder Design for Communication-Efficient Distributed MIMO Receivers With Controlled Peak-Average Power Ratio

We consider the problem of communicating over a relay-assisted multiple-input multiple-output (MIMO) channel with additive noise, in which physically separated relays forward quantized information to a central decoder where the transmitted message is to be decoded. We assume that channel state information is available in the transmitter and show that the design of a rational-forcing precoder - a precoder which is matched to the quantizers used in the relays - is beneficial for reducing the symbol error probability. It turns out that for such rational-forcing precoder based systems, there is natural tradeoff between the peak to average power ratio in the transmitter and the rate of communication between the relays and the central decoder. The precoder design problem is formulated mathematically, and several algorithms are developed for realizing this tradeoff. Optimality of the decoder communication rate is shown based on a result in distributed function computation. Numerical and simulation results show that a useful tradeoff can be obtained between the excess decoder communication rate and the peak-average power ratio in the transmitter.

Convolutional auto-encoder based multiple description coding network

When data is transmitted over an unreliable channel, the error of the data packet may result in serious degradation. The multiple description coding (MDC) can solve this problem and save transmission costs. In this paper, we propose a deep multiple description coding network (MDCN) to realize efficient image compression. Firstly, our network framework is based on convolutional auto-encoder (CAE), which include multiple description encoder network (MDEN) and multiple description decoder network (MDDN). Secondly, in order to obtain high-quality reconstructed images at low bit rates, the encoding network and decoding network are integrated into an end-to-end compression framework. Thirdly, the multiple description decoder network includes side decoder network and central decoder network. When the decoder receives only one of the two multiple description code streams, side decoder network is used to obtain side reconstructed image of acceptable quality. When two descriptions are received, the high quality reconstructed image is obtained. In addition, instead of quantization with additive uniform noise, and SSIM loss and distance loss combine to train multiple description encoder networks to ensure that they can share structural information. Experimental results show that the proposed framework performs better than traditional multiple description coding methods.

Multiple Description Coding Based on Convolutional Auto-Encoder

Deep learning, such as convolutional neural networks, has been achieved great success in image processing, computer vision task, and image compression, and has achieved better performance. This paper designs a multiple description coding frameworks based on symmetric convolutional auto-encoder, which can achieve high-quality image reconstruction. First, the image is input into the convolutional auto-encoder, and the extracted features are obtained. Then, the extracted features are encoded by the multiple description coding and split into two descriptions for transmission to the decoder. We can get the side information by the side decoder and the central information by the central decoder. Finally, the side information and the central information are deconvolved by convolutional auto-encoder. The experimental results validate that the proposed scheme outperforms the state-of-the-art methods.

Deterministic Annealing-Based Optimization for Zero-Delay Source-Channel Coding in Networks

This paper studies the problem of global optimization of zero-delay source-channel codes that map between the source space and the channel space, under a given transmission power constraint and for the mean square error distortion. Particularly, we focus on two well known network settings: the Wyner-Ziv setting where only a decoder has access to side information and the distributed setting where independent encoders transmit over independent channels to a central decoder. Prior work derived the necessary conditions for optimality of the encoder and decoder mappings, along with a greedy optimization algorithm that imposes these conditions iteratively, in conjunction with the heuristic noisy channel relaxation method to mitigate poor local minima. While noisy channel relaxation is arguably effective in simple settings, it fails to provide accurate global optimization in more complicated settings considered in this paper. We propose a powerful non-convex optimization method based on the concept of deterministic annealing -- which is derived from information theoretic principles and was successfully employed in several problems including vector quantization, classification and regression. We present comparative numerical results that show strict superiority of the proposed method over greedy optimization methods as well as prior approaches in literature.

Rate/distortion optimization in multiple description video coding

Multiple Description Coding (MDC) is a technique where multiple streams from a video source are generated, each individually decodable and mutually refinable. MDC is a promising solution to overcome packet loss in video transmission over noisy channels, particularly for real-time applications in which retransmission of lost information is not practical. The error resiliency feature of MDC is achieved at the cost of redundancy, and the required amount of redundancy for each frame depends on the packet loss ratio and also the importance of the frame in the sequence. Due to the error propagation in video transmission over lossy channels, reference frames of a Group of Pictures (GOP) are more important for video reconstruction and, hence, need more redundancy to increase the chance of being received correctly. Therefore a channel adaptive optimization for frame-wise redundancy allocation is needed. In this paper, based on the difference of the side and central decoder outputs, the receiver side distortion is formulated and then used for optimization of a MDC scheme. The performance of the optimizer is verified by experimental results measured from JM 16.0, H.264/AVC reference software.

On polar multiple description coding

The use of multiple description (MD) provides a reliable communications solution over packet loss channels; the polar source coding takes advantage of reasonable complexities in both the encoder and decoder sides. In this study, a polar MD coding is introduced for the binary input sequence with Hamming distortion in which the central decoder is aided by a common description from the encoder. The algorithm for code construction is studied. The performance evaluation demonstrates the effectiveness of the proposed system for MD with polar coding. In addition, the effects of design parameters have been studied.

Robust Uplink Communications over Fading Channels with Variable Backhaul Connectivity

Two mobile users communicate with a central decoder via two base stations. Communication between the mobile users and the base stations takes place over a Gaussian interference channel with constant channel gains or quasi-static fading. Instead, the base stations are connected to the central decoder through orthogonal finite-capacity links, whose connectivity is subject to random fluctuations. There is only receive-side channel state information, and hence the mobile users are unaware of the channel state and of the backhaul connectivity state, while the base stations know the fading coefficients but are uncertain about the backhaul links' state. The base stations are oblivious to the mobile users' codebooks and employ compress-and-forward to relay information to the central decoder. Upper and lower bounds are derived on average achievable throughput with respect to the prior distribution of the fading coefficients and of the backhaul links' states. The lower bounds are obtained by proposing strategies that combine the broadcast coding approach and layered distributed compression techniques. The upper bound is obtained by assuming that all the nodes know the channel state. Numerical results confirm the advantages of the proposed approach with respect to conventional non-robust strategies in both scenarios with and without fading.

Joint Reconstruction of Multiview Compressed Images

Distributed representation of correlated multiview images is an important problem that arises in vision sensor networks. This paper concentrates on the joint reconstruction problem where the distributively compressed images are decoded together in order to take benefit from the image correlation. We consider a scenario where the images captured at different viewpoints are encoded independently using common coding solutions (e.g., JPEG) with a balanced rate distribution among different cameras. A central decoder first estimates the inter-view image correlation from the independently compressed data. The joint reconstruction is then cast as a constrained convex optimization problem that reconstructs total-variation (TV) smooth images, which comply with the estimated correlation model. At the same time, we add constraints that force the reconstructed images to be as close as possible to their compressed versions. We show through experiments that the proposed joint reconstruction scheme outperforms independent reconstruction in terms of image quality, for a given target bit rate. In addition, the decoding performance of our algorithm compares advantageously to state-of-the-art distributed coding schemes based on motion learning and on the DISCOVER algorithm.

A Multiple Description Video Codec With Adaptive Residual Distributed Coding

Multiple description coding (MDC) decomposes one single media into several descriptions and transmits them over different channels for error resilience. Each description contributes to improving the reconstructed media quality when decoded. Distributed video coding (DVC) encodes multiple correlated images and utilizes error correction codes to shift the codec complexity to a joint decoder. Combining MDC with DVC (MDVC) yields a stable codec for mobile encoders. In this paper, to improve the MDVC codec performance, image correlations among the MDVC processing modules were exploited to improve reconstructed video quality and enhance transmission robustness. At the side encoder, a DVC-based adaptive differential pulse code modulation was designed to remove interframe redundancy to enhance rate-distortion performances. For the MDVC central decoding, intradescription and interdescription correlations were utilized to dynamically select the best reconstructed frames from two descriptions, instead of selecting just one description or all key-frames from two descriptions. Experiments showed that, as compared to previous methods, the proposed MDVC control method yielded 1-2 dB higher in image PSNRs for Wyner-Ziv reconstructed frames at the side decoder when encoding low-to-medium complexity videos. For high-complexity videos, it effectively prevents error correction of Wyner-Ziv frames from malfunctioning and yields about 3 dB higher in PSNR. The proposed MDVC central decoder control yields 1-4 dB higher PSNRs, as compared to side decoders. Under lossy transmission, it demonstrates 27-64% smaller PSNR variations, as compared to that of combining key-frames as the decoded video. The proposed MDVC system and control not only improve the DVC reconstructed video quality, but also reduce the quality fluctuation artifacts of MDC coded video for mobile coders.

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.

Joint source-decoding in large scale sensor networks using Markov random field models

An approach to scalable joint source decoding in large-scale sensor networks, based on Markov-random filed (MRF) modeling of the spatio-temporal correlation in the observations is presented. This approach exploits the correlation among a multitude of sensors for joint decoding at a central decoder, while using simple distributed quantizers in individual sensors. The decoder derivations are provided for Slepian–Wolf coded quantization based on both sample-by-sample (scalar) binning and vector binning schemes constructed via channel code partitioning. Simulation results are presented to demonstrate the performance achievable with the proposed decoding approach.

New Results on Multiple Descriptions in the Wyner-Ziv Setting

Some new results of multiple descriptions in the Wyner-Ziv setting (MDWZ) are presented. MDWZ uses two pieces of side information (SI) for two different side decoders. Both SI are available to the central decoder, but none to the encoder. The complete achievable region for a quadratic Gaussian MDWZ system is derived. This result is an extension of Ozarow's result on multiple descriptions of a Gaussian source without SI and Diggavi-Vaishampayan's result on common SI. It is shown that a refinement layer for the central decoder is indispensable if the central distortion is sufficiently small. The tight rate distortion region is also given if one of the side decoders is required to perfectly reconstruct a deterministic function of the source.

Error-Resilient Multiple Description Coding

In the multiple description coding (MDC), the source is decomposed into two or more descriptions, and then transmitted over on/off channels. At the receiver end, each description can be reproduced with acceptable side distortion; reconstructed signals with lower central distortion can be accomplished by exploiting more descriptions. In order to apply the classical MDC over error-prone channels, we propose error-resilient multiple description coding (ERMDC) to utilize the inherent dependency and redundancy between descriptions to combat bit errors. In this paper, we develop a pair of the ERMDC encoder and central decoder based on the multiple description scalar quantizer (MDSQ). After deriving optimal and suboptimal ERMDC decoders, a novel index assignment algorithm is provided for the ERMDC encoder to improve the error detection capability of the ERMDC decoders. Analytical and experimental results show that the ERMDC outperforms the conventional MDSQ encoder-decoder pair, and achieves lower reconstruction distortions than the side distortion within a large range of bit error rates (BERs).

Improving the Performance of Multiple Description Coding Based on Scalar Quantization

We propose an algorithm that improves the performance of rate-distortion-based multiple description coding (RD-MDC). The gain is particularly significant in the high redundancy region, where RD-MDC suffers a major performance penalty with respect to MDC bounds. The improvement is obtained with negligible additional computational cost, by exploiting the coarse information also at the central decoder. The proposed method can be generalized to all MDC schemes that use scalar quantization, without modifying the quantizer structure. This feature guarantees the generation of descriptions that can be decoded without any modification of the decoder.

Multiple description coding by successive quantization

Two structured multiple description (MD) vector quantization schemes with an iterative technique for designing the codebooks and partitions are proposed. The schemes are derived from the recent theoretical work by Chen et al. In the first scheme, the central decoder is formed by the weighted sum of the side codebooks, whereas the second scheme employs the optimum central decoder. The objective of the proposed iterative method is to minimize a Lagrangian cost function (defined as the weighted sum of the central and side distortions) to jointly design the side codebooks and find the associated partitions. The optimal parameters for minimizing the central distortion are also found. Simulations demonstrate that the proposed methods achieve performance close to that of the unstructured, full-search MD quantizer with considerably less complexity and with only a few iterations.

Distributed Coding of Highly Correlated Image Sequences with Motion-Compensated Temporal Wavelets

This paper discusses robust coding of visual content for a distributed multimedia system. The system encodes independently two correlated video signals and reconstructs them jointly at a central decoder. The video signals are captured from a dynamic scene, where each signal is robustly coded by a motion-compensated Haar wavelet. The efficiency of the decoder is improved by a disparity analysis of the first image pair of the sequences, followed by disparity compensation of the remaining images of one sequence. We investigate how this scene analysis at the decoder can improve the coding efficiency. At the decoder, one video signal is used as side information to decode efficiently the second video signal. Additional bitrate savings can be obtained with disparity compensation at the decoder. Further, we address the theoretical problem of distributed coding of video signals in the presence of correlated video side information. We utilize a motion-compensated spatiotemporal transform to decorrelate each video signal. For certain assumptions, the optimal motion-compensated spatiotemporal transform for video coding with video side information at high rates is derived. It is shown that the motion-compensated Haar wavelet belongs to this class of transforms. Given the correlation of the video side information, the theoretical bitrate reduction for the distributed coding scheme is investigated. Interestingly, the efficiency of multiview side information is dependent on the level of temporal decorrelation: for a given correlation SNR of the side information, bitrate savings due to side information are decreasing with improved temporal decorrelation.

Joint source–channel decoder for data-centric network applications

Data-centric networks are gaining importance because of their application in several areas such as sensor networks. One of the key challenges in such networks is the resource constraint under which the nodes must operate. Under these circumstances, the trend is to shift a significant percentage of the computational burden from the individual nodes to a central decoder. The problem of error-resilient communications in data-centric networks is addressed by taking a joint source–channel decoding (JSCD) approach. This improvement in error resilience is achieved by using the source statistics at the decoder, thereby ensuring that the encoder complexity is not increased. Specifically, an optimal JSCD is designed for multiple-access source code (MASC)-encoded Markov sources. This is the first known attempt at designing a JSCD for the general case of correlated sources with memory. Simulation results demonstrate that the proposed decoder outperforms the traditional MASC decoder by up to 10 dB in some cases. The results indicate that significant error resilience is possible even without using forward error-correcting codes at the encoder, which is important in encoder-resource constrained applications.