Operational rate-distortion performance for joint source andchannel coding of images

This paper describes a methodology for evaluating the operational rate-distortion behavior of combined source and channel coding schemes with particular application to images. In particular, we demonstrate use of the operational rate-distortion function to obtain the optimum tradeoff between source coding accuracy and channel error protection under the constraint of a fixed transmission bandwidth for the investigated transmission schemes. Furthermore, we develop information-theoretic bounds on performance for specific source and channel coding systems and demonstrate that our combined source-channel coding methodology applied to different schemes results in operational rate-distortion performance which closely approach these theoretical limits. We concentrate specifically on a wavelet-based subband source coding scheme and the use of binary rate-compatible punctured convolutional (RCPC) codes for transmission over the additive white Gaussian noise (AWGN) channel. Explicit results for real-world images demonstrate the efficacy of this approach.     

Source-channel rate allocation for progressive transmission of images

Progressive image transmission is difficult in the presence of a noisy channel, mainly due to the propagation of errors during the decoding of a progressive bitstream. Excellent results for this problem are made possible through combined source-channel coding, a method that matches the channel code to the source operational rate distortion as well as channel conditions. This paper focuses on the key component of combined source-channel coding: rate allocation. We develop a parametric methodology for rate allocation in progressive source-channel coding. The key to this technique is an empirical model of decoded bit-error rate as a function of the channel code rate. We investigate several scenarios. In the case of the memoryless channel, we present closed-form expressions. For the fading channel and channels with feedback, where closed-form results are elusive, our analysis leads to low-complexity algorithms. The results presented are applicable to any progressive source code, and any family of channel codes.     

Joint source-channel coding for motion-compensated DCT-based SNR scalable video

In this paper, we develop an approach toward joint source-channel coding for motion-compensated DCT-based scalable video coding and transmission. A framework for the optimal selection of the source and channel coding rates over all scalable layers is presented such that the overall distortion is minimized. The algorithm utilizes universal rate distortion characteristics which are obtained experimentally and show the sensitivity of the source encoder and decoder to channel errors. The proposed algorithm allocates the available bit rate between scalable layers and, within each layer, between source and channel coding. We present the results of this rate allocation algorithm for video transmission over a wireless channel using the H.263 Version 2 signal-to-noise ratio (SNR) scalable codec for source coding and rate-compatible punctured convolutional (RCPC) codes for channel coding. We discuss the performance of the algorithm with respect to the channel conditions, coding methodologies, layer rates, and number of layers.     

Subband coding of images using vector quantization

A novel two-dimensional subband coding technique is presented that can be applied to images as well as speech. A frequency-band decomposition of the image is carried out by means of 2D separable quadrature mirror filters, which split the image spectrum into 16 equal-rate subbands. These 16 parallel subband signals are regarded as a 16-dimensional vector source and coded as such using vector quantization. In the asymptotic case of high bit rates, a theoretical analysis yields that a lower bound to the gain is attainable by choosing this approach over scalar quantization of each subband with an optimal bit allocation. It is shown that vector quantization in this scheme has several advantages over coding the subbands separately. Experimental results are given, and it is shown the scheme has a performance that is comparable to that of more complex coding techniques     

Robust image transmission over energy-constrained time-varyingchannels using multiresolution joint source-channel coding

We explore joint source-channel coding (JSCC) for time-varying channels using a multiresolution framework for both source coding and transmission via novel multiresolution modulation constellations. We consider the problem of still image transmission over time-varying channels with the channel state information (CSI) available at (1) receiver only and (2) both transmitter and receiver being informed about the state of the channel, and we quantify the effect of CSI availability on the performance. Our source model is based on the wavelet image decomposition, which generates a collection of subbands modeled by the family of generalized Gaussian distributions. We describe an algorithm that jointly optimizes the design of the multiresolution source codebook, the multiresolution constellation, and the decoding strategy of optimally matching the source resolution and signal constellation resolution “trees” in accordance with the time-varying channel and show how this leads to improved performance over existing methods. The real-time operation needs only table lookups. Our results based on a wavelet image representation show that our multiresolution-based optimized system attains gains on the order of 2 dB in the reconstructed image quality over single-resolution systems using channel optimized source coding     

Combined source-channel coding schemes for video transmission overan additive white Gaussian noise channel

There has been an increased interest in the transmission of digital video over real-world transmission media, such as the direct broadcast satellite (DBS) channel. Video transmitted over such a channel is subject to degradation due, in part, to additive white Gaussian noise (AWGN). Some form of forward error-control (FEC) coding may be applied in order to reduce the effect of the noise on the transmitted bitstream; however, determination of the appropriate level of FEC coding is generally an unwieldy and computationally intensive problem, as it may depend upon a variety of parameters such as the type of video, the available bandwidth, and the channel SNR. More specifically, a combined source-channel coding approach is necessary in optimally allocating rate between source and channel coding subject to a fixed constraint on overall transmission bandwidth. In this paper we develop a method of optimal bit allocation under the assumption that the distortion is additive and independent on a frame-by-frame basis. A set of universal operational distortion-rate characteristics is developed which balances the tradeoff between source coding accuracy and channel error protection for a fixed overall transmission rate and provides the basis for the optimal bit allocation approach. The results for specific source and channel coding schemes show marked improvement over suboptimum choices of channel error protection. In addition, we show that our results approach information-theoretic performance bounds which are developed in this work     

Optimal resource allocation for wireless video over CDMA networks

We present a multiple-channel video transmission scheme in wireless CDMA networks over multipath fading channels. We map an embedded video bitstream, which is encoded into multiple independently decodable layers by 3D-ESCOT video coding technique, to multiple CDMA channels. One video source layer is transmitted over one CDMA channel. Each video source layer is protected by a product channel code structure. A product channel code is obtained by the combination of a row code based on rate compatible punctured convolutional code (RCPC) with cyclic redundancy check (CRC) error detection and a source-channel column code, i.e., systematic rate-compatible Reed-Solomon (RS) style erasure code. For a given budget on the available bandwidth and total transmit power, the transmitter determines the optimal power allocations and the optimal transmission rates among multiple CDMA channels, as well as the optimal product channel code rate allocation, i.e., the optimal unequal Reed-Solomon code source/parity rate allocations and the optimal RCPC rate protection for each channel. In formulating such an optimization problem, we make use of results on the large-system CDMA performance for various multiuser receivers in multipath fading channels. The channel is modeled as the concatenation of wireless BER channel and a wireline packet erasure channel with a fixed packet loss probability. By solving the optimization problem, we obtain the optimal power level allocation and the optimal transmission rate allocation over multiple CDMA channels. For each CDMA channel, we also employ a fast joint source-channel coding algorithm to obtain the optimal product channel code structure. Simulation results show that the proposed framework allows the video quality to degrade gracefully as the fading worsens or the bandwidth decreases, and it offers improved video quality at the receiver.     

Progressive transmission of images over memoryless noisy channels

An embedded source code allows the decoder to reconstruct the source progressively from the prefixes of a single bit stream. It is desirable to design joint source-channel coding schemes which retain the capability of progressive reconstruction in the presence of channel noise or packet loss. Here, we address the problem of joint source-channel coding of images for progressive transmission over memoryless bit error or packet erasure channels. We develop a framework for encoding based on embedded source codes and embedded error correcting and error detecting channel codes. For a target transmission rate, we provide solutions and an algorithm for the design of optimal unequal error/erasure protection. Three performance measures are considered: the average distortion, the average peak signal-to-noise ratio, and the average useful source coding rate. Under the assumption of rate compatibility of the underlying channel codes, we provide necessary conditions for progressive transmission of joint source-channel codes. We also show that the unequal error/erasure protection policies that maximize the average useful source coding rate allow progressive transmission with optimal unequal protection at a number of intermediate rates     

Coding and Common Reconstruction

This work studies problems of source and joint source-channel coding under the requirement that the encoder can produce an exact copy of the compressed source constructed by the decoder. This requirement, termed here as the common reconstruction constraint (CR), is satisfied automatically in rate-distortion theory for single sources. However, in the common formulation of problems of lossy source coding with side information at the decoder (the Wyner-Ziv problem), distributed source coding, and joint source-channel coding for networks, the destination can exploit the information it receives in a manner that cannot be exactly reproduced at the sender side. Some applications, like the transmission of sensitive medical information, may require that both sides-the sender and the receiver-will share a common version of the compressed data, for the purpose of future discussions or consulting. The purpose of this work is to study the implications of CR constraints on the achievable rates in scenarios of lossy source coding and lossy transmission of sources. Three problems are examined: source coding with side information at the decoder, simultaneous transmission of data and state over state-dependent channels, and joint source-channel coding for the degraded broadcast channel. Single-letter characterizations of the optimal performance are developed for these problems, under corresponding CR constraints. Implications of this constraint on problems of joint source-channel coding in networks are discussed.     

Subband image coding using entropy-coded quantization over noisychannels

Under the assumption of noiseless transmission the authors develop two entropy-coded subband image coding schemes. The difference between these schemes is the procedure used for encoding the lowest frequency subband: predictive coding is used in one system and transform coding in the other. After demonstrating the unacceptable sensitivity of these schemes to transmission noise, the authors also develop a combined source/channel coding scheme in which rate-compatible convolutional codes are used to provide protection against channel noise. A packetization scheme to prevent infinite error propagation is used and an algorithm for optimal assignment of bits between the source and channel encoders of different subbands is developed. It is shown that, in the presence of channel noise, these channel-optimized schemes offer dramatic performance improvements     

Transport of wireless video using separate, concatenated, and jointsource-channel coding

The transmission of video over time-varying wireless communication channels can benefit from the use of joint source-channel (JSC) coding methods. We survey relevant research in JSC code design and discuss how this work can be used for video compression and transmission. A main focus is the use of estimation-based techniques to take advantage of the residual redundancy present at the output of the source encoder. As noted in previous work, the combination of the source encoder and channel can often be modeled as a hidden Markov model. This statistical framework is the basis for state estimation and minimum mean-square error estimation procedures for JSC decoding, and it can also be used to develop channel state and channel parameter estimation methods. We discuss these approaches, along with work that also incorporates modulation, channel coding, and rate allocation within the JSC design. The integration of these methods into video compression standards is considered     

Unequal error protection for MPEG-2 video transmission over wireless channels

This paper proposes an unequal error protection (UEP) method for MPEG-2 video transmission. Since the source and channel coders are normally concatenated, if the channel is noisy, more bits are allocated to channel coding and fewer to source coding. The situation is reversed when the channel conditions are more benign. Most of the joint source channel coding (JSCC) methods assume that the video source is subband coded, the bit error sensitivity of the source code can be modeled, and the bit allocations for different subband channels will be calculated. The UEP applied to different subbands is the rate compatible punctured convolution channel coder. However, the MPEG-2 coding is not a subband coding, the bit error sensitivity function for the coded video can no longer be applied. Here, we develop a different method to find the rate-distortion functions for JSCC of the MPEG-2 video. In the experiments, we show that the end-to-end distortion of our UEP method is smaller than the equal error protection method for the same total bit-rate.     

Slepian-Wolf coding over broadcast channels

We discuss reliable transmission of a discrete memoryless source over a discrete memoryless broadcast channel, where each receiver has side information (of arbitrary quality) about the source unknown to the sender. When there are K=2 receivers, the optimum coding strategy using separate and stand-alone source and channel codes is to build two independent binning structures and send bin indices using degraded message sets through the channel, yielding a full characterization of achievable rates. However, as we show with an example, generalization of this technique to multiple binning schemes does not fully resolve the K>2 case. Joint source-channel coding, on the other hand, allows for a much simpler strategy (i.e., with no explicit binning) yielding a successful single-letter characterization of achievable rates for any Kges2. This characterization, which utilizes a trivial outer bound to the capacity region of general broadcast channels, is in terms of marginal source and channel distributions rather than a joint source-channel distribution. This contrasts with existing results for other multiterminal scenarios and implies that optimal schemes achieve "operational separation." On the other hand, it is shown with an example that an optimal joint source-channel coding strategy is strictly advantageous over the combination of stand-alone source and channel codes, and thus "informational separation" does not hold     

Combined source-channel coding for the transmission of still imagesover a code division multiple access (CDMA) channel

This letter considers a combined source-channel coding scheme for image transmission over the uplink of a wireless IS-95 code division multiple access (CDMA) channel using discrete cosine transform. By adjusting the dimension of the orthogonal signaling scheme, we trade the system error-correction capability for a faster bit rate. The increase in channel error is relieved by employing a set of quantizers which are designed using a joint source-channel optimization algorithm     

Analysis of linear prediction, coding, and spectral estimation fromsubbands

Linear prediction schemes make a prediction xˆ_i of a data sample x_i using p previous samples. It has been shown by Woods and O'Neil (1986) as well as Pearlman (1991) that as the order of prediction p→∞, there is no gain to be obtained by coding subband samples. This paper deals with the less well understood theory of finite-order prediction and optimal coding from subbands which are generated by ideal (brickwall) filtering of a stationary Gaussian source. We first prove that pth-order prediction from subbands is superior to pth-order prediction in the fullband, when p is finite. This fact adduces that optimal vector p-tuple coding in the subbands is shown to offer quantifiable gains over optimal fullband p-tuple coding, again when p is finite. The properties of subband spectra are analyzed using the spectral flatness measure. These results are used to prove that subband DPCM provides a coding gain over fullband DPCM, for finite orders of prediction. In addition, the proofs provide means of quantifying the subband advantages in linear prediction, optimal coding, and DPCM coding in the form of gain formulas. Subband decomposition of a source is shown to result in a whitening of the composite subband spectrum. This implies that, for any stationary source, a pth-order prediction error filter (PEF) can be found that is better than the pth-order PEF obtained by solving the Yule-Walker equations resulting from the fullband data. We demonstrate the existence of such a “super-optimal” PEF and provide algorithmic approaches to obtaining this PEF. The equivalence of linear prediction and AR spectral estimation is then exploited to show theoretically, and with simulations, that AR spectral estimation from subbands offers a gain over fullband AR spectral estimation     

Motion-compensated 3-D subband coding of video

This paper describes a video coding system based on motion-compensated three-dimensional (3-D) subband/wavelet coding (MC-3DSBC), which can overcome the limits of both 3-D SBC and MC prediction-based coding. In this new system, spatio-temporal subbands are generated by MC temporal analysis and a spatial wavelet transform, and then encoded by 3-D subband-finite state scalar quantization (3DSB-FSSQ). The rate allocation from the GOP level to each class of subbands is optimized by utilizing the structural property of MC-3DSBC that additive superposition approximately holds for both rate and distortion. The proposed video coding system is applied to several test video clips. Its performance exceeds that of both a known MPEG-1 implementation and a similar subband MC predictive coder while maintaining modest computational complexity and memory size.     

Fixed distortion subband coding of images for packet-switchednetworks

A subband image codec is presented that approximately attains a user-prescribed fidelity by allowing the encoder's compression rate to vary. The fixed distortion subband coding (FDSBC) system is suitable for use with future of packet-switched networks. The codec's design is based on an algorithm that allocates distortion among the subbands to minimize channel entropy. By coupling this allocation procedure with judiciously selected subband quantizers, an elementary four-band codec was obtained. Additional four-band structures may be nested in a hierarchical configuration for improved performance. Each of the configurations tested attains mean square distortions within 2.0 dB of the user-specific value over a wide range of distortion for several standard test images. Rate versus mean-square distortion performance rivals that of fixed-rate systems having similar complexity. The encoder's output is formatted to take advantage of prioritized packet networks. Simulations show that FDSBC is robust with respect to packet loss and may be used effectively for progressive transmission applications     

Tree coding of image subbands

The authors consider the encoding of image subbands with a tree code that is asymptotically optimal for Gaussian sources and the mean squared error (MSE) distortion measure. They first prove that optimal encoding of ideally filtered subbands of a Gaussian image source achieves the rate distortion bound for the MSE distortion measure. The optimal rate and distortion allocation among the subbands is a by-product of this proof. A bound is derived which shows that subband coding is closer than full-band coding to the rate distortion bound for a finite length sequence. The tree codes are then applied to encode the image subbands, both nonadaptively and adaptively. Since the tree codes are stochastic and the search of the code tree is selective, a relatively few reproduction symbols may have an associated squared error a hundred times larger than the target for the subband. Correcting these symbols through a postcoding procedure improves the signal-to-noise ratio and visual quality significantly, with a marginal increase in total rate.     

Fiber-optic subcarrier multiplexed CDMA local-area networks forsubband image transmission

This paper investigates the application of subcarrier multiplexed code-division-multiple-access (CDMA) techniques to image transmission over fiber-optic local-area networks (LANs). In the hybrid scheme, CDMA is used to suppress the interference caused by the laser nonlinearity in the subcarrier multiplexing (SCM) fiber-optic communication systems. Likewise, the SCM scheme is able to increase the channel data rate of CDMA systems. This hybrid system combines the advantages of both schemes and is particularly well suited to subband coding that divides the image information into multiple parallel data streams using an analysis filter bank, each of which is transmitted via a unique subcarrier-code pair, where the spreading code and subcarrier frequency correspond to the image and one of its subbands, respectively. This hybrid scheme also allows more than one image to be transmitted and be accessed simultaneously at the same channel bandwidth, in which each image is assigned a particular spreading code added to its digital data modulating the subcarrier. After transmission, each received signal is independently recovered at a high-Q surface acoustic wave (SAW) receiver with the matching subcarrier-code pair. Other concurrent signals are rejected by the SAW. Then, all the recovered subbands are reassembled by a synthesis filter bank into a close reproduction to the original image. The image quality of subband image transmission via CDMA/SCM fiber-optic channels is evaluated and examined     

Region adaptive subband image coding

We present a region adaptive subband image coding scheme using the statistical properties of image subbands for various subband decompositions. Motivated by analytical results obtained when the input signal to the subband decomposition is a unit step function, we analyze the energy packing properties toward the lower frequency subbands, edges, and the dependency of energy distribution on the orientation of the edges, in subband decomposed images. Based on these investigations and ideal analysis/synthesis filtering done in the frequency domain, the region adaptive subband image coding scheme extracts suitably shaped regions in each subband and then uses adaptive entropy-constrained quantizers for different regions under the assumption of a generalized Gaussian distribution for the image subbands. We also address the problem of determining an optimal subband decomposition among all possible decompositions. Experimental results show that visual degradations in the reconstructed image are negligible at a bit rate of 1.0 b/pel and reasonable quality images are obtainable at rates as low as 0.25 b/pel.