Elements of information theory
Elements of information theory
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
Efficient Broadcasting Protocols for Video on Demand
MASCOTS '98 Proceedings of the 6th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Computer Networking: A Top-Down Approach (4th Edition)
Computer Networking: A Top-Down Approach (4th Edition)
Joint Source-Channel Coding at the Application Layer
DCC '09 Proceedings of the 2009 Data Compression Conference
Joint source-channel coding at the application layer for parallel Gaussian sources
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 3
Hybrid digital-analog (HDA) joint source-channel codes for broadcasting and robust communications
IEEE Transactions on Information Theory
To code, or not to code: lossy source-channel communication revisited
IEEE Transactions on Information Theory
Capacity of wireless erasure networks
IEEE Transactions on Information Theory
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In this paper, the multicasting of digitally encoded images on a heterogeneous network is considered. In order to obtain analytically tractable problems, the wavelet transform coefficients of a digital image are modeled as a set of parallel Gaussian sources. Also, a general network transport mechanism subject to packet losses is modeled as an erasure broadcast channel where users are affected by possibly very different erasure probabilities. In the proposed setting, the convex nature of the rate distortion function allows relevant optimization problems corresponding to various performance criteria to be solved. The solutions of these optimization problems serve as starting points for the design of source-channel codes based on embedded scalar quantization, on linear rateless encoders that map directly the (redundant) bits generated by the quantizer into channel symbols, and on progressive transmission of the encoded symbols organized into "layers", such that users with higher capacity achieve better end-to-end distortion. At the decoders, iterative belief propagation decoding, multi-stage sequential decoding of the layers and soft-bit reconstruction are used. Numerical experiments show that 1) the proposed model is sufficiently accurate to provide system design guidelines for the case of real-life images, and 2) the proposed coding scheme achieves rate-distortion performance very close to the theoretical optimum.