Source Encoding Research Articles

Discriminatory Lossy Source Coding: Side Information Privacy

A lossy source coding problem is studied in which a source encoder communicates with two decoders, one with and one without correlated side information with an additional constraint on the privacy of the side information at the uninformed decoder. Two cases of this problem arise depending on the availability of the side information at the encoder. The set of all feasible rate-distortion-equivocation tuples is characterized for each case. The difference between the informed and uninformed cases and the advantages of encoder side information for enhancing privacy are highlighted for a binary symmetric source with erasure side information and Hamming distortion.

Multi‐layer transmission and hybrid relaying for relay channels with multiple out‐of‐band relays

ABSTRACTIn this work, a relay channel is studied in which a source encoder communicates with a destination decoder through a number of out‐of‐band relays that are connected to the decoder through capacity‐constrained digital backhaul links. This model is motivated by the uplink of cloud radio access networks. In this scenario, a novel transmission and relaying strategies are proposed in which multi‐layer transmission is used, on the one hand, to adaptively leverage the different decoding capabilities of the relays and, on the other hand, to enable hybrid decode‐and‐forward and compress‐and‐forward relaying. The hybrid relaying strategy allows each relay to forward part of the decoded messages and a compressed version of the received signal to the decoder. The problem of optimising the power allocation across the layers and the compression test channels is formulated. Albeit non‐convex, the derived problem is found to belong to the class of so‐called complementary geometric programs. Using this observation, an iterative algorithm based on the homotopy method is proposed that achieves a stationary point of the original problem by solving a sequence of geometric programming, and thus convex, problems. Numerical results are provided that show the effectiveness of the proposed multi‐layer hybrid scheme in achieving performance close to a theoretical (cutset) upper bound. Copyright © 2013 John Wiley & Sons, Ltd.

The effect of object processing in content-dependent source memory

BackgroundPrevious studies have suggested that the study condition of an item influences how the item is encoded. However, it is still unclear whether subsequent source memory effects are dependent upon stimulus content when the item and context are unitized. The present fMRI study investigated the effect of encoding activity sensitive to stimulus content in source memory via unitization. In the scanner, participants were instructed to integrate a study item, an object in either a word or a picture form, with perceptual context into a single image.ResultsSubsequent source memory effects independent of stimulus content were identified in the left lateral frontal and parietal regions, bilateral fusiform areas, and the left perirhinal cortex extending to the anterior hippocampus. Content-dependent subsequent source memory effects were found only with words in the left medial frontal lobe, the ventral visual stream, and bilateral parahippocampal regions. Further, neural activity for source memory with words extensively overlapped with the region where pictures were preferentially processed than words, including the left mid-occipital cortex and the right parahippocampal cortex.ConclusionsThese results indicate that words that were accurately remembered with correct contextual information were processed more like pictures mediated by integrated imagery operation, compared to words that were recognized with incorrect context. In contrast, such processing did not discriminate subsequent source memory with pictures. Taken together, these findings suggest that unitization supports source memory for both words and pictures and that the requirement of the study task interacts with the nature of stimulus content in unitized source encoding.

Frequency-domain auto-adapting full waveform inversion with blended source and frequency-group encoding

As a high quality seismic imaging method, full waveform inversion (FWI) can accurately reconstruct the physical parameter model for the subsurface medium. However, application of the FWI in seismic data processing is computationally expensive, especially for the three-dimension complex medium inversion. Introducing blended source technology into the frequency-domain FWI can greatly reduce the computational burden and improve the efficiency of the inversion. However, this method has two issues: first, crosstalk noise is caused by interference between the sources involved in the encoding, resulting in an inversion result with some artifacts; second, it is more sensitive to ambient noise compared to conventional FWI, therefore noisy data results in a poor inversion. This paper introduces a frequency-group encoding method to suppress crosstalk noise, and presents a frequency-domain auto-adapting FWI based on source-encoding technology. The conventional FWI method and source-encoding based FWI method are combined using an auto-adapting mechanism. This improvement can both guarantee the quality of the inversion result and maximize the inversion efficiency.

Polar codes for distributed source coding

A polar coding method to construct a distributed source coding scheme which can achieve any point on the dominant face of the Slepian-Wolf rate region for sources with uniform marginals is proposed. Source encoding and decoding operations are performed using efficient algorithms which makes practical implementation feasible. Simulation results are given to exhibit the performance of the presented method.

Bit-Plane Coding of Lattice Codevectors

In a sign-magnitude representation of binary lattice codevectors, only a few least significant bit-planes are constrained due to the structure of the lattice, while there is no restriction on other more significant bit-planes. Hence, any convenient bit-plane coding method can be used to encode the lattice codevectors, with modification required only for the lattice-defining, least-significant bit-planes. Simple encoding methods for the lattice-defining bit-planes of the D4, RE8, and Barnes-Wall 16-dimensional lattices are described. Simulation results for the encoding of a uniform source show that standard bit-plane coding together with the proposed encoding provide about the same performance as integer lattice vector quantization when the bit-stream is truncated. When the entire bit-stream is fully decoded, the granular gain of the lattice is realized.

Low Bit-Rate Encoding Algorithm for Distributed Speech Recognition Based on SVD Decomposition

The paper presents an algorithm for compression of front-end feature extracted parameters used in Distributed Speech Recognition (DSR). In the proposed method the source encoder is mainly based on truncated Singular Value Decomposition transform (SVD) with conventional vector and scalar quantizers. The system provides a compression bit-rate around 3500 bps. The experiments were carried out on the TIDigitsAurora-2 database using Hidden Makcov Model Toolkit (HTK). The simulation results show good recognition performance without dramatic change, comparing toconventional ETSI Aurorastandard front-end feature compression algorithm with quantized features at 4400 bps.

Comparison of the neural correlates of encoding item-item and item-context associations

fMRI was employed to investigate the role of the left inferior frontal gyrus (LIFG) in the encoding of item-item and item-context associations. On each of a series of study trials subjects viewed a picture that was presented either to the left or right of fixation, along with a subsequently presented word that appeared at fixation. Memory was tested in a subsequent memory test that took place outside of the scanner. On each test trial one of two forced choice judgments was required. For the associative test, subjects chose between the word paired with the picture at study and a word studied on a different trial. For the source test, the judgment was whether the picture had been presented on the left or right. Successful encoding of associative information was accompanied by subsequent memory effects in several cortical regions, including much of the LIFG. By contrast, successful source encoding was selectively associated with a subsequent memory effect in right fusiform cortex. The finding that the LIFG was enhanced during successful associative, but not source, encoding is interpreted in light of the proposal that subsequent memory effects are localized to cortical regions engaged by the on-line demands of the study task.

Analytical Performance Evaluation of Hierarchical 16 QAM for Multicast/Broadcast Transmission

An information theoretical approach is applied to analyze the performance of hierarchical 16 quadrature amplitude modulation (QAM). Using the approach, it is quantitatively evaluated how much the hierarchical 16 QAM outperforms non-hierarchical, conventional 16 QAM. More specifically, two kinds of performance comparisons are examined. First, the performance of the hierarchical QAM combined with layered source encoding (such as scalable video coding) is compared to that of the non-hierarchical QAM without layered source encoding. Second, the former is compared to the performance of the non-hierarchical QAM, also with layered source encoding.

Transforming Monitoring Structures with Resilient Encoders—Application to Repeated Games

An important feature of a dynamic game is its monitoring structure namely, what the players effectively see from the played actions. We consider games with arbitrary monitoring structures. One of the purposes of this paper is to know to what extent an encoder, who perfectly observes the played actions and sends a complementary public signal to the players, can establish perfect monitoring for all the players. To reach this goal, the main technical problem to be solved at the encoder is to design a source encoder which compresses the action profile in the most concise manner possible. A special feature of this encoder is that the multi-dimensional signal (namely, the action profiles) to be encoded is assumed to comprise a component whose probability distribution is not known to the encoder and the decoder has a side information (the private signals received by the players when the encoder is off). This new framework appears to be both of game-theoretical and information-theoretical interest. In particular, it is useful for designing certain types of encoders that are resilient to single deviations and provide an equilibrium utility region in the proposed setting; it provides a new type of constraints to compress an information source (i.e., a random variable). Regarding the first aspect, we apply the derived result to the repeated prisoner's dilemma.

Energy Management Policies for Energy-Neutral Source-Channel Coding

In cyber-physical systems where sensors measure the temporal evolution of a given phenomenon of interest and radio communication takes place over short distances, the energy spent for source acquisition and compression may be comparable with that used for transmission. Additionally, in order to avoid limited lifetime issues, sensors may be powered via energy harvesting and thus collect all the energy they need from the environment. This work addresses the problem of energy allocation over source acquisition/compression and transmission for energy-harvesting sensors. At first, focusing on a single-sensor, energy management policies are identified that guarantee a maximal average distortion while at the same time ensuring the stability of the queue connecting source and channel encoders. It is shown that the identified class of policies is optimal in the sense that it stabilizes the queue whenever this is feasible by any other technique that satisfies the same average distortion constraint. Moreover, this class of policies performs an independent resource optimization for the source and channel encoders. Analog transmission techniques as well as suboptimal strategies that do not use the energy buffer (battery) or use it only for adapting either source or channel encoder energy allocation are also studied for performance comparison. The problem of optimizing the desired trade-off between average distortion and delay is then formulated and solved via dynamic programming tools. Finally, a system with multiple sensors is considered and time-division scheduling strategies are derived that are able to maintain the stability of all data queues and to meet the average distortion constraints at all sensors whenever it is feasible.

Multiple-Source Slepian-Wolf Coding Under a Linear Equation Correlation Model

In this work we present practical coding schemes for the problem of lossless distributed source coding for multiple sources. We consider two scenarios - the classical Slepian-Wolf case where there is no feedback from the terminal to the sources and a case where there is feedback from the terminal to the source encoders. The correlation model of interest is given by a system of linear equations, a generalization of the work of Stankovic et al. '06. We propose a transformation of correlation model and a way to determine proper decoding schedules, both of which are required to obtain the optimal sum rate. Our scheme allows us to exploit more correlations than those in the previous work. Simulation results show that the proposed coding scheme has lower sum rate than previous work in both scenarios.

Robust Transmission of Compressed HTML Files over Wireless Channel using an Iterative Joint Source-Channel Decoding Receiver

This paper proposes an algorithm for the robust reception of compressed HTML files transmitted over a noisy mobile radio channel. Both source encoders and transmission systems are assumed to be standard compliant. The source encoder follows the HTTP1.1 protocol specifications, i.e. the HTML files are encoded by the deflate algorithm, a combination of Lempel-Ziv and Huffman algorithms. The transmission scheme follows IEEE 802.11a (and 802.11n) standard as an example. The proposed receiver is based on an iterative joint source-channel decoding approach. The Soft-Input Soft-Output outer source decoder is based on a sequential M-algorithm, which has been modified to improve the decoding performance by exploiting the specific grammatical and syntax rules of (i) Huffman codes; (ii) Lempel-Ziv codes; and (iii) HTML language. Simulation results following the IEEE 802.11a (and 802.11n) standard over additive white Gaussian noise and Rayleigh fading channels show that the proposed receiver drastically reduces the number of errors occurring in the received HTML files compared to the classical receivers. An EXIT chart analysis illustrates some properties of this combination of source and channel decoders.

Cascade and Triangular Source Coding With Side Information at the First Two Nodes

We consider the cascade and triangular rate-distortion problem where side information is known to the source encoder and to the first user but not to the second user. We characterize the rate-distortion region for these problems, as well as some of their extensions. For the quadratic Gaussian case, we show that it is sufficient to consider jointly Gaussian distributions, which leads to an explicit solution.

Fast randomized full-waveform inversion with compressive sensing

Wave-equation-based seismic inversion can be formulated as a nonlinear least-squares problem. The demand for higher-resolution models in more geologically complex areas drives the need to develop techniques that exploit the special structure of full-waveform inversion to reduce the computational burden and to regularize the inverse problem. We meet these goals by using ideas from compressive sensing and stochastic optimization to design a novel Gauss-Newton method, where the updates are computed from random subsets of the data via curvelet-domain sparsity promotion. Two different subset sampling strategies are considered: randomized source encoding, and drawing sequential shots firing at random source locations from marine data with missing near and far offsets. In both cases, we obtain excellent inversion results compared to conventional methods at reduced computational costs.

LSPIHT Algorithm for ECG Data Compression and Transmission for Telemedicine Applications

Medical image analysis and data compression are rapidly evolving fields with growing applications in healthcare services e.g. teleradiology, teleconsultation, e-health, telemedicine and statistical analysis of medical data. In this paper, a Layered Set Partitioning in Hierarchical Trees (LSPIHT) algorithm for medical data compression and transmission is presented. In the LSPIHT, the encoded bit streams are divided into a number of layers for transmission and reconstruction. Starting from the base layer, by accumulating bit streams up to different enhancement layers, medical data can be reconstructed with various signal-to-noise ratios (SNRs) and resolutions. Receivers with distinct specifications can then share the same source encoder to reduce the complexity of telecommunication networks for telemedicine applications. The algorithm is compared with other algorithms for encoding ECG data, and analysis shows that the LSPIHT attains better rate-distortion performance and low network complexity than other encoding techniques.DOI: http://dx.doi.org/10.3329/dujs.v60i1.10332 Dhaka Univ. J. Sci. 60(1): 31-36, 2012 (January)

Optimal and robust communication for a uniform source

A communication system based on the joint source-channel coding principle is proposed, where a fixed-rate source encoder is used to avoid the error propagation effect as experienced when variable length coding is used and thus gain system robustness. An explicit expression of the mean square error (MSE) distortion of the system for a uniform source is derived. We provide an algorithm to achieve the optimal solution by investigating the Karush–Kuhn–Tucker (KKT) condition. The numerical results show that the overall performance of the proposed system is close to the performance of the entropy coded scalar quantiser (ECSQ) system established by A. György and T. Under for almost all rate region. A 0.176-bits-per-sample performance loss is found for the worst case by comparing our considered system and ECSQ system. An observation resembling the finding by V. Goya1 et al. is that in the low rate region, there exist certain regimes in which the proposed system outperforms the ECSQ system. Finally, the proposed system outperforms the equal rate system with a large margin.

Iterative Source–Channel Decoding With Reduced Error Floors

Audio-visual source encoders for digital mobile communications extract parameters that-due to delay and complexity constraints-exhibit some residual redundancy. This residual redundancy can be exploited by iterative source-channel decoding (ISCD) to improve the robustness against transmission noise by performing soft parameter detection as part of the decoding process. Systems employing ISCD at the receiving end often exhibit an observable error floor. While this error floor can be tolerated in some cases, it is often desirable to perfectly reconstruct the source codec parameters. In this paper, we explain the reasons for the error floor and propose two solutions for realizing ISCD systems with optimized error floor performance while maintaining the desired near-capacity waterfall behavior. All approaches aim at optimizing the distance properties of the (redundant) mapping of bit patterns to the source codec parameters. In some cases, especially if small quantizer code books are employed, good mappings cannot be found-in this case, the novel multi-dimensional bit mapping allows to reduce the error floor after decoding.

Iconicity of sequence in source and goal encoding in two Papuan languages of south-east Indonesia

Iconicity of sequence in source and goal encoding in two Papuan languages of south-east Indonesia

Concatenated Block Codes for Unequal Error Protection of Embedded Bit Streams

A state-of-the-art progressive source encoder is combined with a concatenated block coding mechanism to produce a robust source transmission system for embedded bit streams. The proposed scheme efficiently trades off the available total bit budget between information bits and parity bits through efficient information block size adjustment, concatenated block coding, and random block interleavers. The objective is to create embedded codewords such that, for a particular information block, the necessary protection is obtained via multiple channel encodings, contrary to the conventional methods that use a single code rate per information block. This way, a more flexible protection scheme is obtained. The information block size and concatenated coding rates are judiciously chosen to maximize system performance, subject to a total bit budget. The set of codes is usually created by puncturing a low-rate mother code so that a single encoder-decoder pair is used. The proposed scheme is shown to effectively enlarge this code set by providing more protection levels than is possible using the code rate set directly. At the expense of complexity, average system performance is shown to be significantly better than that of several known comparison systems, particularly at higher channel bit error rates.