Source Coding

Multiple description (MD) coding is source coding in which several descriptions of the source are produced such that various reconstruction qualities are obtained from different subsets of the descriptions. Unlike multiresolution or layered source coding, there is no hierarchy of descriptions; thus, MD coding is suitable for packet erasure channels or networks without priority provisions. Generalizing work by Orchard, Wang, Vaishampayan, and Reibman, a transform-based approach is developed for producing descriptions of an -tuple source, . The descriptions are sets of transform coefficients, and the transform coefficients of different descriptions are correlated so that missing coefficients can be estimated. Several transform optimization results are presented for memoryless Gaussian sources, including a complete solution of the = 2, = 2 case with arbitrary weighting of the descriptions. The technique is effective only when independent components of the source have differing variances. Numerical studies show that this method performs well at low redundancies, as compared to uniform MD scalar quantization.

Data-mining over software can reveal similar patterns on software code. This can give important insights for the design of hardware cores, especially considering the benefits of the merge of software kernels and their implementation as a single hardware core. However, software codes have characteristics that make inadequate the direct use of typical data mining tools, mainly related to their large number of samples and the imprecise definition of code features for mining. Those characteristics affect negatively the performance of the most known data mining methods. To solve this problem, we propose in this paper the use of three techniques: the Normalized Compression Distance, the Neighbor Joining, and the Fast Newman algorithm. We combine these three techniques and propose a new approach for data mining of code repositories (DAMICORE). DAMICORE works with different types of code representations. Experiments reveal DAMICORE can indicate important software similarities at source code level. Specifically, merging software kernels identified by DAMICORE results in FPGA cores with size smaller than the overall hardware size needed when implementing a core for each kernel.

We study the two-way communication problem for the relay channel. Hereby, two terminals communicate simultaneously in both directions with the help of one relay. We consider the restricted two-way problem, i.e., the encoders at both terminals do not cooperate. We provide achievable rate regions for different cooperation strategies, such as decode-and-forward based on block Markov superposition coding and compress-andforward based on Wyner-Ziv source coding. We also evaluate the regions for the special case of additive white Gaussian noise channels. We show that a combined strategy of block Markov superposition coding and Wyner-Ziv coding achieves the cut-set upper bound on the sum-rate of the two-way relay channel when the relay is in the proximity of one of the terminals.

Abstract-We describe a sequential universal data compression procedure for binary tree sources that performs the “double mixture.” Using a context tree, this method weights in an ef-ficient recursive way the coding distributions corresponding to all bounded memory tree sources, and ...

In the recent years, Internet multimedia applications have become very popular. Valuable multimedia content such as digital images, however, is vulnerable to unauthorized access while in storage and during transmission over a network. Streaming digital images also require high network bandwidth for transmission. For effective image transmission over Internet; therefore, both security and bandwidth issues must be considered. In this paper, we present a novel scheme, which combines Discrete Wavelet Transform (DWT) for image compression and block cipher Data Encryption Standard (DES) for image encryption. The simulation results indicate thaf our proposed method enhances the security for image transmission over the Internet as well as improves the transmission rate.

I N THE first part of this two-part paper, a structure and hierarchy were developed for tracing the key signal processing steps of a typical digital communications system. With the structure as a guide, formatting, source coding, and modulation transformations were examined. Also treated were potential trade-offs for power-limited and bandwidth-limited systems. In Part II, the signal processing overview continues with channel coding, multiplexing and multiple access, frequency spreading, encryption, and synchronization. To complete the overview, fundamental link analysis relationships are reviewed in the context of a satellite repeater channel.

Transmission energy allocation (TEA) to bits according to their sensitivity is known to significantly enhance robustness to channel errors. These advantages are gained at the cost of high peak-to-average ratio (PAR) of the signal energy employed to transmit different bits. We show that, in the case of 4-QAM, appropriate grouping of bits allows achieving all the gains of optimal TEA while maintaining PAR at a small fraction of a decibel. Alternatively, we show how to achieve close to optimal TEA under the constraint of perfect (0 dB) PAR, thus extending the application of TEA to constant envelope modulation schemes. Performance is illustrated with an example of Gauss-Markov sources compressed by vector quantization.

Virtual Sliding Window" algorithm presented in this paper is an adaptive mechanism for estimating the probability of ones at the output of binary non-stationary sources. It is based on "Imaginary sliding window" idea proposed by B.Ryabko. The proposed algorithm was used as an alternative adaptation mechanism in Context-Based Adaptive Binary Arithmetic Coding (CABAC) -an entropy coding scheme of H.264/AVC standard for video compression. The "virtual sliding window" algorithm was integrated into an open-source codec supporting H.264/AVC standard. Comparison of the "virtual sliding window" algorithm with the original adaptation mechanism from CABAC is presented. Test results for standard video sequences are included. These results indicate that using the proposed algorithm improves rate-distortion performance compared to the original CABAC adaptation mechanism. Besides improvement in rate-distortion performances the "Virtual Sliding Window" algorithm has one more advantage. CABAC uses a finite state machine (FSM) for estimation of the probability of ones at the output of a binary source. Transitions for FSM are defined by a table stored in memory. The disadvantage of CABAC consists in frequent reference to this table (one time for every binary symbol encoding), which is critical for DSP implementation. The "Virtual Sliding Window" algorithm allows to avoid using the table of transitions 1 .

Most source coding standards (voice, audio, image and video) use Variable-Length Codes (VLCs) for compression. However, the VLC decoder is very sensitive to transmission errors in the compressed bit-stream. Previous contributions, using a trellis description of the VLC codewords to perform soft decoding, have been proposed. Significant improvements are achieved by this approach when compared with prefix decoding. Nevertheless, for realistic VLCs, the complexity of the trellis technique becomes intractable. In this paper, we propose a soft-input VLC decoding method using an a priori knowledge of the lengths of the sourcesymbol sequence and the compressed bit-stream with Maximum A Posteriori (MAP) sequence estimation. Performance in the case of transmission over an Additive White Gaussian Noise (AWGN) channel is evaluated. Simulation results show that the proposed decoding algorithm leads to significant performance gain in comparison with the prefix VLC decoding besides exhibiting very low complexity. A new VLC decoding method generating additional information regarding the reliability of the bits of the compressed bit-stream is also proposed. We consider the serial concatenation of a VLC with two types of channel code and perform iterative decoding. Results show that, when concatenated with a recursive systematic convolutional code (RSCC), iterative decoding provides remarkable error correction performance. In fact, a gain of about 2.3 dB is achieved, in the case of transmission over an AWGN channel, with respect to tandem decoding. Second, we consider a concatenation with a low-density parity-check (LDPC) code and it is shown that iterative joint source/channel decoding outperforms tandem decoding and an additional coding gain of 0.25 dB is achieved.

Industry is using power meters to monitor the consumption of energy and achieving cost savings. This monitoring often involves energy metering software with a web interface. However, web applications often have vulnerabilities that can be exploited by cyber-attacks. We present an approach and a tool to solve this problem by analyzing the application source code and automatically inserting fixes to remove the discovered vulnerabilities. We demonstrate the use of the tool with two open source energy metering applications in which it found and corrected 17 vulnerabilities. By looking in more detail into some of these vulnerabilities, we argue that they are very serious, leading to the following impacts: violation of user privacy, counter the benefits of energy metering, and serve as entering points for attacks on other user software.

The interleaver, which combines the outputs of turbo decoders, is essential in determining the performance of turbo codes and is the main source of decoder computation and implementation complexity. ABSTRACT | The discovery of turbo codes and the subsequent rediscovery of low-density parity-check (LDPC) codes represent major milestones in the field of channel coding. Recent advances in the design and theory of turbo codes and their relationship to LDPC codes are discussed. Several new interleaver designs for turbo codes are presented which illustrate the important role that the interleaver plays in these codes. The relationship between turbo codes and LDPC codes is explored via an explicit formulation of the parity-check matrix of a turbo code, and simulation results are given for sum product decoding of a turbo code.

Code repositories of the version control systems have been used as important raw material in lots of developer behavior studies. In such research, commits are usually considered as a unit of development phases, even though they contain limited information over development process. Unlike former source code management systems, Git provides branching, which are the unit of concurrent and independant development activities in a whole project. Branches have significant advantages over commits from the viewpoint of the developer behavior researchers. By exploiting the characteristics of branching, we propose a Git branch analysis tool. The proposed tool automatically extracts branching data from Git repositories and performs statistical and graphical analysis on them. Through a few case studies using a number of popular OSS project repositories, we showed that the proposed branch-oriented analyzer is able to perform more insightful analysis than the existing commit-based analyzers.

With the increasing use of multimedia technologies, image compression requires higher performance as well as new features. To address this need in the specific area of still image encoding, a new standard is currently being developed the JPEG2000. It is not only intended to provide rate-distortion and subjective image quality performance superior to existing standards, but also to provide functionality that current standards can either not address efficiently or not address at all. Lossless and lossy compression, encoding of very large images, progressive transmission by pixel accuracy and by resolution, robustness to the presence of bit-errors and region-of-interest coding, are some representative examples of its features. It is interesting to note that JPEG2000 is being designed to address the requirements of a diversity of applications, e.g. Internet, colour facsimile, printing, scanning, digital photography, remote sensing, mobile applications, medical imagery, digital library and Ecommerce.

This paper presents an efficient structured binning scheme for solving the noiseless distributed source coding problem with parallel concatenated convolutional codes, or turbo codes. The novelty in the proposed scheme is the introduction of a syndrome former and an inverse syndrome former to efficiently and optimally exploit an existing turbo code without the need to redesign or modify the code structure and/or decoding algorithms. Extension of the proposed approach to serially concatenated codes is also briefed and examples including conventional turbo codes and asymmetric turbo codes are given to show the efficiency and the general applicability of the approach. Simulation results reveal good performance which is close to theoretic limit.

Content Management Systems (CMS) are typically regarded as critical software platforms for the success of organizational web sites and intranets. Although most current CMS systems allow their extension through the addition of modules/components, these are usually built using the typical source-code-oriented software development process, which is slow and error-prone. On the other hand, a MDEoriented development process is centered on models, which represent the system and are used to automatically generate all corresponding artifacts, such as source-code and documentation.

This paper considers the problem of scalable distributed coding of correlated sources that are communicated to a central unit. The general setting is typically encountered in sensor networks. The conditions of communication channels between the source encoders and fusion center may be time-varying and it is often desirable to guarantee a base layer of coarse information during channel fades. In addition the desired system should be robust to various scenarios of channel failure and should utilize all the available information to attain the best possible compression efficiency. Although a standard 'Lloyd-style' distributed coder design algorithm can be generalized to scalable distributed source coding, the resulting algorithm depends heavily on initialization and will virtually always converge to a poor local minimum on the distortion-cost surface. We propose an efficient initialization scheme for such a system, which employs a properly designed multi-stage distributed coder. In our prior work, we highlighted the fundamental conflict that arises when multi-stage coding is directly combined with distributed quantization. Here we use the multi-stage distributed coding system to initialize a scalable distributed coder and propose an iterative algorithm for joint design of all system components once the structural constraint is removed. Simulation results show considerable gains over randomly initialized scalable distributed coder design.

This paper presents a class of new lossless data compression algorithms. Each algorithm in this class first transforms the original data to be compressed into an irreducible table representation and then uses an arithmetic code to compress the irreducible table representation. F'rom the irreducible table representation, one can fully reconstruct the original data by performing multistage parallel substitution. A set of rules is described on how to perform hierarchical transformations from the original data to irreducible table representations. Theoretically, it is proved that all these algorithms outperform any finite state sequential compression algorithm and hence achieve the ultimate compression rate for any stationary and ergodic soupce. Furthermore, experiments on several standard images show that even a simple algorithm in this class, the so-called multi-level pattern matching algorithm, outperforms the Lempel-Ziv algorithms and arithmetic codes.

In the recent years, Internet multimedia applications have become very popular. Valuable multimedia content such as digital images, however, is vulnerable to unauthorized access while in storage and during transmission over a network. Streaming digital images also require high network bandwidth for transmission. For effective image transmission over Internet; therefore, both security and bandwidth issues must be considered. In this paper, we present a novel scheme, which combines Discrete Wavelet Transform (DWT) for image compression and block cipher Data Encryption Standard (DES) for image encryption. The simulation results indicate thaf our proposed method enhances the security for image transmission over the Internet as well as improves the transmission rate.

Commencing with the brief history of mobile communications and the portrayal of the basic concept of wireless multimedia communications, the implications of Shannon's theorems regarding joint source and channel coding for wireless communications are addressed. Following a brief introduction to speech, video, and graphical source coding as well as the cellular concept, a rudimentary overview of flexible, reconfigurable, mobile radio schemes is provided. We then summarize the fundamental concepts of modulation, introduce an adaptive modem scheme, and argue that third-generation transceivers might become adaptively reconfigurable under network control in order to meet backward compatibility requirements with existing systems and to achieve best compromise among a range of conflicting system requirements in terms of communications quality, bandwidth requirements, complexity and power consumption, robustness against channel errors, etc.

An achievable region for the -channel multiple description coding problem is presented. This region generalizes twochannel results of El Gamal and Cover and of Zhang and Berger. It further generalizes three-channel results of Gray and Wyner and of Zhang and Berger. A source that is successively refinable on chains is shown to be successively refinable on trees. A new outer bound on the rate-distortion (RD) region for memoryless Gaussian sources with mean squared error distortion is also derived. The achievable region meets this outer bound for certain symmetric cases.

This paper studies an application of turbo codes to compressed image/video transmission and presents an approach to improving error control performance through joint channel and source decoding (JCSD). The proposed approach to JCSD includes error-free source information feedback, error-detected source information feedback, and the use of channel soft values (CSV) for source signal postprocessing. These feedback schemes are based on a modification of the extrinsic information passed between the constituent maximum a posteriori probability (MAP) decoders in a turbo decoder. The modification is made according to the source information obtained from the source signal processor. The CSVs are considered as reliability information on the hard decisions and are further used for error recovery in the reconstructed signals. Applications of this joint decoding technique to different visual source coding schemes, such as spatial vector quantization, JPEG coding, and MPEG coding, are examined. Experimental results show that up to 0.6 dB of channel SNR reduction can be achieved by the joint decoder without increasing computational cost for various channel coding rates.

In many multiterminal communication problems, constructions of good source codes involve finding distributed partitions (into bins) of a collection of quantizers associated with a group of source encoders. Further, computationally efficient procedures to index these bins are also required. In this work, we consider a constructive approach for distributed binning in an algebraic framework. Several application scenarios fall under the scope of this paper including the CEO problem, distributed source coding, and n-channel symmetric multiple description source coding with n>2. Specifically, in this exposition we consider the case of two codebooks while focusing on the Gaussian CEO problem with mean squared error reconstruction and with two symmetric observations. This problem deals with distributed encoding of correlated noisy observations of a source into descriptions such that the joint decoder having access to them can reconstruct the source with a fidelity criterion. We employ generalized coset codes constructed in a group-theoretic setting for this approach, and analyze the performance in terms of distance properties and decoding algorithms.

Generalized multiple description coding (GMDC) is source coding for multiple channels such that a decoder which receives an arbitrary subset of the channels may produce a useful reconstruction. This paper reports on applications of two recently proposed methods for GMDC to image coding. The first produces statistically correlated streams such that lost streams can be estimated from the received data. The second uses quantized frame expansions and hence is conceptually similar to block channel coding, except it is done prior to quantization

Real world source coding algorithms usually leave a certain amount of redundancy within the coded bit stream. Shannon already mentioned that this redundancy can be exploited at the receiver side to achieve a higher robustness against channel errors. In this paper, we show how joint source-channel decoding can be performed in a way that is applicable to any mobile communication system standard. Considerable gains in terms of bit error rate or signal-to-noise ratio (SNR) are possible dependent on the amount of redundancy. However, an even better performance can be achieved by changing also the transmitter sided source and channel encoders. We propose an encoding concept employing low-dimensional quantization. Keeping the gross bit rate as well as the clean channel quality the same, it decreases the complexity of the source encoder and the decoder significantly. Finally, we give an application of our methods to spectral coefficient coding in speech transmission over a Rayleigh fading channel resulting in channel SNR gains of about 2 dB as compared to state-of-the-art (de-)coding and bad frame handling methods.

In this paper we provide sufficient conditions for lossy transmission of functions of correlated data over a multiple access channel (MAC). The conditions obtained can be shown as generalized version of Yamamoto's result [28]. We also obtain efficient joint source-channel coding schemes for transmission of discrete and continuous alphabet sources to recover the function values.

The method of types is one of the key technical tools in Shannon Theory, and this tool is valuable also in other fields. In this paper, some key applications will be presented in sufficient detail enabling an interested nonspecialist to gain a working knowledge of the method, and a wide selection of further applications will be surveyed. These range from hypothesis testing and large deviations theory through error exponents for discrete memoryless channels and capacity of arbitrarily varying channels to multiuser problems. While the method of types is suitable primarily for discrete memoryless models, its extensions to certain models with memory will also be discussed. Index Terms-Arbitrarily varying channels, choice of decoder, counting approach, error exponents, extended type concepts, hypothesis testing, large deviations, multiuser problems, universal coding.

Classically, the filter banks (FB's) used in source coding schemes have been chosen to possess the perfect reconstruction (PR) property or to be maximally selective quadrature mirror filters (QMF's). This paper puts this choice back into question and solves the problem of minimizing the reconstruction distortion, which, in the most general case, is the sum of two terms: a first one due to the non-PR property of the FB and the other being due to signal quantization in the subbands. The resulting filter banks are called minimum mean square error (MMSE) filter banks. In this paper, several quantization noise models are considered. First, under the classical white noise assumption, the optimal positive bit rate allocation in any filter bank (possibly nonorthogonal) is expressed analytically, and an efficient optimization method of the MMSE filter banks is derived. Then, it is shown that while in a PR FB, the improvement brought by an accurate noise model over the classical white noise one is noticeable, it is not the case for MMSE FB. The optimization of the synthesis filters is also performed for two measures of the bit rate: the classical one, which is defined for uniform scalar quantization, and the orderone entropy measure. Finally, the comparison of rate-distortion curves (where the distortion is minimized for a given bit rate budget) enables us to quantify the SNR improvement brought by MMSE solutions.

The coding rate of a one-shot Tunstall code for stationary and memoryless sources is investigated in non-universal situations so that the probability distribution of the source is known to the encoder and the decoder. When studying the variable-to-fixed length code, the average coding rate has been defined as (i) the codeword length divided by the average block length. We define the average coding rate as (ii) the expectation of the pointwise coding rate, and prove that (ii) converges to the same value as (i). key words: lossless data compression, VF code, Tunstall code, definition of average coding rate, pointwise redundancy rate

Refactoring is widely recognized as a way to improve the internal structure of a software system in order to ensure its long-term maintainability. Consequently, software projects which adopt refactoring practices should see reductions in the complexity of their code base. We evaluated this assumption on an open source system -namely PMD, a Java source code analyzer-and discovered that periods of refactorings did not affect the cyclomatic complexity. This paper investigates this counterintuitive phenomenon through a detailed analysis of the actual source code manipulations applied on the system under study.

To meet the requirement of distributed video coding in resource-limited sensor networks, Wyner-Ziv theorem-based source coding with side information available at the decoder states that an intraframe encoder with interframe decoder system can achieve comparable coding efficiency of a conventional video codec. Most existing Wyner-Ziv video coding systems are with light encoder and heavy decoder. In this paper, a new content-aware media hash-based Wyner-Ziv video codec with light encoder and light decoder is proposed. The key is that the significant differences between a video frame and its reference frame are efficiently extracted and used for frame recovery based on robust image hashing without needing to perform motion estimation. The particular contribution of our method is its low complexity in both the encoder and decoder sides. Simulation results demonstrate the achievable coding efficiency of our method in particular for videos with small and middle motions.

Code revision of a leading telecom product was performed, combining manual audit and static analysis tools. On average, one exploitable vulnerability was found for every 4000 lines of code. Half of the located threats in the product were buffer overflows followed by race condition, misplaced trust, and poor random generators. Static analysis tools were used to speed up the revision process and to integrate security tests into the overall project process. The discussion analyses the effectiveness of automatic tools for auditing software. Furthermore, the incorporation of the software security analysis into the development process, and the results and costs of the security analysis is discussed. From the initial 42 workdays used for finding all vulnerabilities, approximately 16 days were needed for finding and correcting 91,5 % of the vulnerabilities. So, proportionally small investments improve the program code security by integrating an automatic auditing tool into the ordinary execution of source code revision.

ABSTRACT A generalized notion of source divisibility or in other words successive refinement of information with an additional requirement of exponential decrease of error probability is considered. A necessary and sufficient condition for the possibility of such successive refinement is established

The potential benefits of traceability are well known, as well as the impracticability of recovering and maintaining traceability links manually. Indeed, the manual management of traceability information is an error prone and time consuming task. Consequently, despite the advantages that can be gained, explicit traceability is rarely established unless there is a regulatory reason for doing so. Extensive efforts have been brought forth to improve the explicit connection of software artifacts in the software engineering community (both research and commercial). Promising results have been achieved using Information Retrieval (IR) techniques for traceability recovery. IR-based traceability recovery methods propose a list of candidate traceability links based on the similarity between the text contained in the software artifacts. Software artifacts have different structures and the common element among many of them is the textual data, which most often captures the informal semantics of artifacts. For example, source code includes large volume of textual data in the form of comments and identifiers. In consequence, IRbased approaches are very well suited to address the traceability recovery problem. The conjecture is that artifacts with high textual similarity are good candidate to be traced to each other since they share several concepts. In this chapter we overview a general process of using IR-based methods for traceability link recovery and overview some of them in a greater detail: probabilistic, vector space and Latent Semantic Indexing models. Finally, we discuss common approaches to measuring the performances of IR-based traceability methods and the latest advances in techniques for analysis of candidate links.

This paper studies the asymptotic characteristics of uniform scalar quantizers that are optimal with respect to mean squared error. It is shown that when a symmetric source density with infinite support is sufficiently well behaved, the optimal step size ∆ N for symmetric uniform scalar quantization decreases as 2σ N −1 V −1 1 / 6N 2 () , where N is the number of quantization levels, σ 2 is the source variance and V −1 (⋅) is the inverse of V (y) = y −1 P(σ −1 X > x) dx y ∞ ∫. Equivalently, the optimal support length N∆ N increases as 2σ V −1 1 / 6N 2 (). Granular distortion is asymptotically well approximated by ∆ N 2 / 12, and the ratio of overload to granular distortion converges to a function of the limit τ ≡ lim y→∞ y −1 E[X| X > y], provided, as usually happens, that τ exists. When it does, its value is related to the number of finite moments of the source density; an asymptotic formula for the overall distortion D N is obtained; and τ = 1 is both necessary and sufficient for the overall distortion to be asymptotically well approximated by ∆ N 2 / 12. Applying these results to the class of two-sided densities of the form b x β e − a x α , which includes Gaussian, Laplacian, Gamma, and generalized Gaussian, it is found that τ = 1, that ∆ N decreases as (ln N) 1/ α / N , that D N is asymptotically well approximated by ∆ N 2 / 12 and decreases as (ln N) 2 / α / N 2 , and that more accurate approximations to ∆ N are possible. The results also apply to densities with one-sided infinite support, such as Rayleigh and Weibull, and to densities whose tails are asymptotically similar to those previously mentioned.

In this demonstration, we present a code search system called Portfolio that retrieves and visualizes relevant functions and their usages. We will show how chains of relevant functions and their usages can be visualized to users in response to their queries.