Source Decoding Research Articles

Stealing bits from a quantized source

We consider "bit stealing" scenarios where the rate of a source code must be reduced without prior planning. We first investigate the efficiency of source requantization to reduce rate, which we term successive degradation. We focus on finite-alphabet sources with arbitrary distortion measures as well as the Gaussian-quadratic and high-resolution scenarios. We show an achievable rate-distortion tradeoff and prove that this is the best guaranteeable tradeoff for any good source code. This tradeoff is in general different from the rate-distortion tradeoff with successive refinement, where there is prior planning. But, we show that with quadratic distortion measures, for all sources with finite differential entropy and at least one finite moment, the gap is at most 1/2 bit or 3 dB in the high-resolution limit. In the Gaussian-quadratic case, the gap is at most 1/2 bit for all resolutions. We further consider bit stealing in the form of information embedding, whereby an embedder acts on a quantized source and produces an output at the same rate and in the original source codebook. We develop achievable distortion-rate tradeoffs. Two cases are considered, corresponding to whether or not the source decoder is informed of the embedding rate. In the Gaussian-quadratic case, we show the informed decoder need only augment the regular decoder with simple post-reconstruction distortion compensation in the form of linear scaling for the resulting system to be as efficient as bit stealing via successive degradation. Finally, we show that the penalty for uninformed versus informed decoders is at most 3 dB or 0.21-bit in the Gaussian-quadratic case and that their performance also lies within the 1/2-bit gap to that of successive refinement.

Turbo compression/joint source–channel coding of correlated binary sources with hidden Markov correlation

We propose a system to perform compression and joint source–channel coding of correlated binary sources when the correlation between the sources is defined by a hidden Markov model. In the case of source coding, punctured turbo codes are used as source encoders, with the objective of compressing the information at rates close to the Slepian–Wolf theoretical limit. The same system structure can be utilized for the transmission of the correlated sources through noisy channels (joint source–channel coding), so that the desired information rates are achieved by puncturing. In both cases, no a priori knowledge about the correlation statistics is required in the encoding process. The source decoder utilizes iterative schemes and does not present significant performance degradation when the correlation parameters are not known in the decoder, since they can be estimated jointly with the iterative decoding process.

Iterative joint source-channel decoding of speech spectrum parameters over an additive white Gaussian noise channel

In this paper, we show how the Gaussian mixture modeling framework used to develop efficient source encoding schemes can be further exploited to model source statistics during channel decoding in an iterative framework to develop an effective joint source-channel decoding scheme. The joint probability density function (PDF) of successive source frames is modeled as a Gaussian mixture model (GMM). Based on previous work, the marginal source statistics provided by the GMM is used at the encoder to design a low-complexity memoryless source encoding scheme. The source encoding scheme has the specific advantage of providing good estimates to the probability of occurrence of a given source code-point based on the GMM. The proposed iterative decoding procedure works with any channel code whose decoder can implement the soft-output Viterbi algorithm that uses a priori information (APRI-SOVA) or the BCJR algorithm to provide extrinsic information on each source encoded bit. The source decoder uses the GMM model and the channel decoder output to provide a priori information back to the channel decoder. Decoding is done in an iterative manner by trading extrinsic information between the source and channel decoders. Experimental results showing improved decoding performance are provided in the application of speech spectrum parameter compression and communication.

Analysis and optimization of irregular LSPC codes for joint source-channel decoding

In this paper, we present a characterization, through its convergence analysis, and an optimisation of a joint source-channel receiver composed of a LDPC decoder and a Soft Input Soft Output (SISO) source decoder. Under Gaussian approximation, assuming the knowledge of the extrinsic mutual information transfer function (EXIT chart) of the source decoder, we derive the Mutual Information evolution equations, that semianalytically describe the convergence of the iterative system behavior and, to complete the study, the stability condition at the convergence fixed point is derived for the joint receiver. From this analysis, a general optimisation method of the irregularity of the LDPC codes is proposed, which can be reduced to a linear programming optimisation problem. Simulation results show improved performance when compared to an AWGN optimized LDPC code.

Turbo decodulation: iterative combined demodulation and source-channel decoding

We propose the combination of iterative demodulation and iterative source-channel decoding as a multiple turbo process. The receiver structures of bit-interleaved coded modulation with iterative decoding (BICM-ID), iterative source-channel decoding (ISCD), and iterative source coded modulation (ISCM) are merged to one novel turbo system, in which in two iterative loops reliability information is exchanged between the three single components, demodulator, channel decoder and (softbit) source decoder. Simulations show quality improvements compared to the different previously known systems, which use iterative processing only for two components of the receiver.

An Efficient SF-ISF Approach for the Slepian-Wolf Source Coding Problem

A simple but powerful scheme exploiting the binning concept for asymmetric lossless distributed source coding is proposed. The novelty in the proposed scheme is the introduction of a syndrome former (SF) in the source encoder and an inverse syndrome former (ISF) in the source decoder to efficiently exploit an existing linear channel code without the need to modify the code structure or the decoding strategy. For most channel codes, the construction of SF-ISF pairs is a light task. For parallelly and serially concatenated codes and particularly parallel and serial turbo codes where this appear less obvious, an efficient way for constructing linear complexity SF-ISF pairs is demonstrated. It is shown that the proposed SF-ISF approach is simple, provenly optimal, and generally applicable to any linear channel code. Simulation using conventional and asymmetric turbo codes demonstrates a compression rate that is only 0.06 bit/symbol from the theoretical limit, which is among the best results reported so far.

Iterative joint source-channel decoding of variable-length codes using residual source redundancy

We present a novel symbol-based soft-input a posteriori probability (APP) decoder for packetized variable-length encoded source indexes transmitted over wireless channels where the residual redundancy after source encoding is exploited for error protection. In combination with a mean-square or maximum APP estimation of the reconstructed source data, the whole decoding process is close to optimal. Furthermore, solutions for the proposed APP decoder with reduced complexity are discussed and compared to the near-optimal solution. When, in addition, channel codes are employed for protecting the variable-length encoded data, an iterative source-channel decoder can be obtained in the same way as for serially concatenated codes, where the proposed APP source decoder then represents one of the two constituent decoders. The simulation results show that this iterative decoding technique leads to substantial error protection for variable-length encoded correlated source signals, especially, when they are transmitted over highly corrupted channels.

Blind Decoding of Multiple Description Codes over OFDM Systems via Sequential Monte Carlo

We consider the problem of transmitting a continuous source through an OFDM system. Multiple description scalar quantization (MDSQ) is applied to the source signal, resulting in two correlated source descriptions. The two descriptions are then OFDM modulated and transmitted through two parallel frequency-selective fading channels. At the receiver, a blind turbo receiver is developed for joint OFDM demodulation and MDSQ decoding. Transformation of the extrinsic information of the two descriptions are exchanged between each other to improve system performance. A blind soft-input soft-output OFDM detector is developed, which is based on the techniques of importance sampling and resampling. Such a detector is capable of exchanging the so-called extrinsic information with the other component in the above turbo receiver, and successively improving the overall receiver performance. Finally, we also treat channel-coded systems, and a novel blind turbo receiver is developed for joint demodulation, channel decoding, and MDSQ source decoding.

Joint Estimation and Compression of Correlated Nonbinary Sources Using Punctured Turbo Codes

We propose a system to perform data compression of correlated nonbinary sources when the correlation between sources is not known, either at the encoder or the decoder. The sequences of nonbinary symbols are transformed into sequences of bits, and then source coded using punctured turbo codes, with the puncturing adjusted to achieve the desired compression rate. Each source is compressed without knowledge about the other source, and the correlation model is not assumed to be known at the encoder. The source decoder uses iterative schemes over the compressed binary sequences, and recovers the nonbinary symbol sequences from both sources. The correlation model between sources does not need to be known at the decoder, since it can be estimated jointly with the iterative decoding process. Compared with the case in which the correlation is known at the decoder, no significant performance loss is observed. The performance of the proposed scheme is close to the Slepian-Wolf theoretical limit.

Link Adaptation for Framed Multimedia Data Transmission over a DS-CDMA Communication System

In the context of frame-based multimedia wireless transmission, a link adaptation strategy is proposed, assuming that the source decoder may accept some remaining errors at the output of the channel decoder. Based on a target mean bit error rate for erroneous frames, a minimum bit-energy-to-equivalent-noise ratio is chosen. Under this constraint, a new link adaptation criterion is proposed: the maximization of the minimum user's information rate through dynamic spreading gain and power control, allowing to guarantee a transmission for each and every user. An analytical solution to this constrained optimization problem is proposed and its performance is studied in a Rayleigh-fading environment.

Soft-Input Reconstruction of Binary Transmitted Quantized Overcomplete Expansions

We propose a soft-decoding method for quantized overcomplete frame expansions that are binary transmitted through noisy channels. The frame expansions can be viewed as real-valued block codes that are directly applied to waveform signals prior to quantization. The explicit redundancy introduced in the continuous amplitude domain is exploited by the decoder in two stages. First, the index-based redundancy is used by a soft-input soft-output source decoding approach that outputs decoded symbols together with their reliability information. In a second stage, the soft information on the symbols and the structure of the introduced redundancy are used to correct errors. The performance of the proposed approach is evaluated for different code constructions based on the discrete Fourier transform (DFT), the discrete cosine transform (DCT), and the discrete Hadamard transform (DHT), and is compared to standard approaches without soft decoding.

Efficient Source Decoding Over Memoryless Noisy Channels Using Higher Order Markov Models

Exploiting the residual redundancy in a source coder output stream during the decoding process has been proven to be a bandwidth-efficient way to combat noisy channel degradations. This redundancy can be employed to either assist the channel decoder for improved performance or design better source decoders. In this work, a family of solutions for the asymptotically optimum minimum mean-squared error (MMSE) reconstruction of a source over memoryless noisy channels is presented when the redundancy in the source encoder output stream is exploited in the form of a /spl gamma/-order Markov model (/spl gamma//spl ges/1) and a delay of /spl delta/,/spl delta/>0, is allowed in the decoding process. It is demonstrated that the proposed solutions provide a wealth of tradeoffs between computational complexity and the memory requirements. A simplified MMSE decoder which is optimized to minimize the computational complexity is also presented. Considering the same problem setup, several other maximum a posteriori probability (MAP) symbol and sequence decoders are presented as well. Numerical results are presented which demonstrate the efficiency of the proposed algorithms.

Joint Source-Channel Decoding of Predictively and Nonpredictively Encoded Sources: A Two-Stage Estimation Approach

A common joint source-channel (JSC) decoder structure for predictively encoded sources involves first forming a JSC decoding estimate of the prediction residual and then feeding this estimate to a standard predictive decoding (synthesis) filter. In this paper, we demonstrate that in a JSC decoding context, use of this standard filter is suboptimal. In place of the standard filter, we choose the synthesis filter coefficients to give a least-squares (LS) estimate of the original source, based on given training data. For first-order differential pulse-code modulation, this yields as much as 0.65-dB gain in reconstructing first-order Gauss-Markov sources. More gains are achieved with modest additional complexity by increasing the filter order. While performance can also be enhanced by increasing the source's Markov model order and/or the decoder's lookup table memory, complexity grows exponentially in these parameters. For both predictive and nonpredictive coding, our LS approach offers a strategy for increasing the estimation accuracy of JSC decoders while retaining manageable complexity.

Robust decoding of variable-length encoded Markov sources using a three-dimensional trellis

In this letter, we present an improved index-based a-posteriori probability (APP) decoding approach for the error-resilient transmission of packetized variable-length encoded Markov sources. The proposed algorithm is based on a novel two-dimensional (2D) state representation which leads to a three-dimensional trellis with unique state transitions. APP decoding on this trellis is realized by employing a 2D version of the BCJR algorithm where all available source statistics can be fully exploited in the source decoder. For an additional use of channel codes the proposed approach leads to an increased error-correction performance compared to a one-dimensional state representation.

Compression of correlated binary sources using turbo codes

We propose the use of punctured turbo codes for compression of correlated binary sources. Compression is achieved because of puncturing. The resulting performance is close to the theoretical limit provided by the Slepian-Wolf (1973) theorem. No information about the correlation between sources is required in the encoding process. The proposed source decoder utilizes iterative schemes, and performs well even when the correlation between the sources is not known in the decoder, since it can be estimated jointly with the iterative decoding process.

Architecture and design of NX-2700: a programmable single-chip HDTV all-format-decode-and-display processor

This paper describes the architecture, functionality, and design of NX-2700, a digital television and media processor chip from Philips Semiconductors. The NX-2700 is the second generation of an architectural family of programmable multimedia processors targeted at the digital television (DTV) markets, including the United States Advanced Television Systems Committee (ATSC) DTV-standard-based applications. The chip not only supports all of the 18 ATSC formats from standard-definition to wide-angle, high-definition video, but has also the power to handle high-definition television (HDTV) video and audio source decoding (high-level MPEG-5 AC-3 and ProLogic audio, closed captioning, etc.) as well as the flexibility to process advanced interactive services. NX-2700 is a programmable processor with a very powerful, general-purpose very long instruction word (VLIW) central processing unit (CPU) core that implements many nontrivial multimedia algorithms, coordinates all on-chip activities, and runs a small real-time operating system. The CPU core, aided by an array of peripheral devices (multimedia coprocessors and input-output units) and high-performance buses, facilitates concurrent processing of audio, video, graphics, and communication-data.

Rate-distortion optimized layered coding with unequal error protection for robust Internet video

We present an effective framework for increasing the error-resilience of low bit-rate video communications over an error-prone packet-switched network. Our framework is based on the principle of layered coding with transport prioritization. We introduce a rate-distortion optimized mode-selection algorithm for our prioritized layered framework. This algorithm is based on a joint source/channel-coding approach and trades off source coding efficiency for increased bitstream error-resilience to optimize the video coding mode selection within and across layers. The algorithm considers the channel conditions, as well as the error recovery and concealment capabilities, of the channel codec and source decoder, respectively. Important framework parameters including the packetization scheme, decoder error concealment method, and channel codec error-protection strength are considered. The effects of mismatch between the parameters employed by the encoder and the actual channel conditions are considered. Results are presented for a wide range of packet loss rates in order to illustrate the benefits of the proposed framework.

Soft source decoding with applications

Motivated by previous results in joint source-channel coding and decoding, we consider the problem of decoding of variable-length codes using soft channel values. We present results of decoding of selected codes using the maximum a posteriori (MAP) decoder and the sequential decoder, and show the performance gains over decoding using hard decisions alone. The objective behind this work is to provide motivation for decoding of data compressed by standard source coding schemes, that is, to view the compressed bitstreams as being the output of variable-length coders and to make use of the redundancy in the bitstreams to assist in decoding. In order to illustrate the performance achievable by soft decoding, we provide results for decoding of MPEG-4 reversible variable-length codes as well as for decoding of MPEG-4 overhead information, under the assumption that this information is transmitted without channel coding over an additive white Gaussian noise channel. Finally, we present a method of unequal error protection for an MPEG-4 bitstream using the MAP and sequential source decoders, and show results comparable to those achievable by serial application of source and channel coding.

Joint turbo decoding and estimation of hidden Markov sources

We describe a joint source-channel scheme for modifying a turbo decoder in order to exploit the statistical characteristics of hidden Markov sources. The basic idea is to treat the trellis describing the hidden Markov source as another constituent decoder which exchanges information with the other constituent decoder blocks. The source block uses as extrinsic information the probability of the input bits that is provided by the constituent decoder blocks. On the other hand, it produces a new estimation of such a probability which will be used as extrinsic information by the constituent turbo decoders. The proposed joint source-channel decoding technique leads to significantly improved performance relative to systems in which source statistics are not exploited and avoids the need to perform any explicit source coding prior to transmission. Lack of a priori knowledge of the source parameters does not degrade the performance of the system, since these parameters can be jointly estimated with turbo decoding.

Turbo codes for image transmission-a joint channel and source decoding approach

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.