3:30, SPCOM-P6.1
SOFT-INPUT SOURCE DECODING FOR ROBUST TRANSMISSION OF COMPRESSED IMAGES USING TWO-DIMENSIONAL OPTIMAL ESTIMATION
J. KLIEWER, N. GOERTZ
In this paper we address the transmission of compressed images over highly corrupted AWGN-channels using an optimal estimation approach at the decoder. In contrast to other methods we only use a negligible amount of explicit redundancy based on channel codes. Mainly, the implicit residual source redundancy inherent in the quantized subband images and the bit-reliability information at the channel output are utilized for error protection. As a novelty we extend the optimal estimation technique from the one- to the two-dimensional case, where both horizontal and vertical correlations are exploited in the subband images. Based on this approach the performances for several estimation methods are compared, where also approaches for approximating the source correlations at the decoder are discussed.

3:30, SPCOM-P6.2
BOUNDS ON THE CHANNEL DISTORTION OF VECTOR QUANTIZERS
G. BEN-DAVID, D. MALAH
Vector-Quantization (VQ) is a widely implemented method for low-bit-rate signal coding. A common assumption in the design of VQ systems is that the digital information is transmitted through a perfect channel. Under this assumption, the assignment of channel symbols to the VQ Reconstruction Vectors (RV) is of no importance. However, under physical channels, the effect of channel errors on the VQ system performance depends on the index assignment of the RV. For a VQ of size N, there are N! possible assignments, meaning that an exhaustive search over all possible assignments is practically impossible. In this paper, lower and upper bounds on the performance of VQ systems under channel errors, over all possible assignments, are presented. A related expression for the average performance is also discussed. Numerical examples are given in which the bounds and average performance are compared with index assignments obtained by the index-switching algorithm.

3:30, SPCOM-P6.3
LOCALLY OPTIMAL JOINT ENCODING OF IMAGE TRANSFORM COEFFICIENTS
P. BUNYARATAVEJ, D. MILLER
We address the choice of encoder for conditional entropy-constrained trellis-coded quantization (CECTCQ), applied to image transform coefficients. The optimal CECTCQ encoder requires an (utterly intractable) exhaustive search and the standard method of greedy, sequential encoding of the coefficient `sources' is suboptimal. Alternatively, we suggest a locally optimal encoding algorithm, guaranteed to improve performance over greedy encoding, and yet with manageable increases in encoding complexity. This method uses dynamic programming as a local optimization encoding `step', repeatedly applied until convergence. Simulations demonstrate up to 1.5 dB gain over greedy CECTCQ encoding of block-transformed images.

3:30, SPCOM-P6.4
SEQUENTIAL SIGNAL ENCODING AND ESTIMATION OF DISTRIBUTED SENSOR NETWORKS
H. PAPADOPOULOS, M. ABDALLAH
We develop algorithms for sequential signal encoding from sensor measurements, and for signal estimation via fusion of channel-corrupted versions of these encodings. For signals described by state space models, we present optimized sequential binary-valued encodings constructed via threshold-controlled scalar quantization of a running Kalman filter signal estimate from the sensor measurements. We also develop methods for robust fusion from observations of these encodings corrupted by binary symmetric channels.

3:30, SPCOM-P6.5
JOINT SOURCE-CHANNEL CODING USING STRUCTURED OVERSAMPLED FILTERS BANKS APPLIED TO IMAGE TRANSMISSION
A. GABAY, P. DUHAMEL, O. RIOUL
This paper proposes a new joint source and channel coder based on the BCH codes on the reals, in which the signal protection (analogous to a signal interpolation) is performed before compresion. This allows a better tradeoff in high accuracy compression, since part of the distortion introduced by the compression can be corrected by the "channel decoder". Furthermore, we propose as an optimal code allocation procedure, which allows to obtain a good robustness, too, when the errors introduced by the channel increase. The resulting rate/distortion curves outperforms those obtained by a separate system on the whole range of operation. Although presented in the context of image transmission, through a Binary Symetric Channel, the resulting codes may be employed on a wide range of transmissions schemes with significant performance benefits.

3:30, SPCOM-P6.6
COMPARISON OF STRUCTURES FOR JOINT EQUALIZATION AND DECODING
D. DECLERCQ
In this paper, we compare two structures for combined Equalization/Detection of linear codes over frequency selective channels. The structures comes from different families of codes: convolutionnal codes on one hand and parity check block codes on the other hand. First, we show that the joint receiver process corresponds to an iterative belief propagation schedule on graphical representations. Then, we draw and comment the simulation results for various codes and channel choices.

3:30, SPCOM-P6.7
AN ALGORITHM FOR TRANSFORM CODING FOR LOSSY PACKET NETWORKS
F. PALMIERI, D. PETRICCIONE
We propose an algorithm to compute a modification of the classical discrete Karhunen-Loeve Transform (KLT) useful when some of the coefficients are randomly unavailable for reconstruction. Such a scheme can provide Multiple Description Coding (MDC) for signals and images transported by lossy packet links. The modification of the KLT is based on a ``correlating'' block that, from knowledge of the channel erasure statistics, is optimized with a gradient algorithm to provide minimum average reconstruction error. A set of simulations show appreciable improvements over standard schemes.

3:30, SPCOM-P6.8
JOINT AND TANDEM SOURCE-CHANNEL CODING WITH DELAY CONSTRAINTS
J. LIM, D. NEUHOFF
Two common source-channel coding strategies, joint and tandem, are compared on the basis of distortion vs. delay by analyzing specific representatives of each when transmitting analog data samples across a binary symmetric channel. Channel-optimized transform coding is the joint source-channel coding strategy; transform coding with Reed-Solomon coding is the tandem strategy. For each strategy, formulas for the mean-squared error and delay are found and used to minimize distortion subject to a delay constraint, for data modeled as Gauss-Markov. The results of such optimizations suggest there is a threshold such that when the permissible delay is above this threshold, tandem coding is better, and when below the threshold, channel-optimized transform coding is better.

3:30, SPCOM-P6.9
VECTORIAL DPCM CODING AND APPLICATION TO WIDEBAND CODING OF SPEECH
D. MARY, D. SLOCK
This paper deals with optimal coding for vectorial signals by means of a decorrelating transform such as DPCM. We show that the optimal causal transform corresponds to a (Lower-Diagonal-Upper) triangular factorization of the autocorrelation matrix of the signal : the transformation matrix is triangular and unit diagonal. Each one of its rows is the optimal prediction filter for the corresponding component of the vector to be coded. We analyze the effect on the coding gain of the perturbation due to backward adaptation (prediction based on the quantized signal), as for DPCM coders. We then show that two previously introduced transformations, in the context of subband coding, appear as special cases of vectorial DPCM coding, and we compare these two transformations when perturbations occur on the reference signal. Finally, whe apply some results of vectorial DPCM coding to wideband speech coding.

3:30, SPCOM-P6.10
LOW-COMPLEXITY TRANSFORM CODING WITH INTEGER-TO-INTEGER TRANSFORMS
C. GIURCANEANU, I. TABUS
In this paper we propose the application of a new transform-based coding method in conjunction with Golomb-Rice (GR) codes to lower significantly the complexity, which can be used in various applications, e.g. the Multiple Description coding. The theoretical evaluations predict no important loss in compression performance, while the complexity is considerably reduced. Since GR codes are very fast and well suited for exponentially decaying distributions, they were implemented during the last decade in image and audio compressors. In all these schemes, the selection of the code parameter is performed presuming Laplacian distribution of prediction errors. We derive the selection method for the GR code parameter also for the case of Gaussian inputs.

3:30, SPCOM-P6.11
INDEXING AND ENTROPY CODING OF LATTICE CODEVECTORS
A. VASILACHE, I. TABUS
We present here two methods of entropy coding for the lattice codevectors. We compare our entropy coding methods with one method previously presented in the literature from the point of view of rate-distortion as well as the computation complexity and memory requirements. The results are presented for artificial Laplacian and Gaussian data as well as for LSF parameters of speech signals. In the latter case the multiple scale lattice VQ (MSLVQ) is used for quantization, which reduces the rate gain compared with the fixed rate case, but allows a dynamic allocation of the bits in the whole speech coding scheme.