Network model coding gain for smoothed descriptions coding

Numerous image coders have been proposed which improve the reconstructed image quality in the presence of transmission errors. Some of the more successful examples involve partitioning the image during the encoding process, and transmitting each `description´ of the image over a separate channel. However, analysis of the performance of such coders, given a network model, has been lacking until when the performance of generic coders and Gaussian sources was investigated. We build on this previous work by analyzing the performance of a specific partitioned coding algorithm called `smoothed descriptions coding´ coupled with a network model. Unlike other commonly considered coders, this coder has the pleasing feature of adding virtually no overhead to the encoded bit-stream. The results show that smoothed description coding can provide a gain over single-description coding. For test images, the largest gain was found to be approximately 7.4 dB at a moderate network loading of ρ=0.4