To code, or not to code: lossy source-channel communication revisited

Information Theory Tutorial Review of ‘To code or not to code - Lossy Source Channel Communication revisited’

In this tutorial, the paper ”To code or not to code Lossy Source Channel Communication revisited’ [1] is reviewed. This paper investigates the conditions under which a source-channel communication system is optimal. To test the optimality of a communication system, it is sufficient to measure the average cost and the average distortion, and check if these quantities lie on the optimal costdisto...

To code, or not to code: lossy source-channel communication revisited

What makes a source–channel communication system optimal? It is shown that in order to achieve an optimal cost–distortion tradeoff, the source and the channel have to be matched in a probabilistic sense. The match (or lack of it) involves the source distribution, the distortion measure, the channel conditional distribution, and the channel input cost function. Closed-form necessary and sufficie...

To code, or not to code?

In summary, it is also important to remember the hidden rules: 1) Just because there is a code in the manual, it doesn't mean it can be billed to insurance, or that once billed, it will be reimbursed. 2) Just because a code was paid once, doesn't mean it will ever be paid again--or that you get to keep the money! 3) The healthcare provider is responsible for knowing all the rules, but then it i...

To Code or Not to Code

It is well known and surprising that the uncoded transmission of an independent and identically distributed Gaussian source across an additive white Gaussian noise channel is optimal: No amount of sophistication in the coding strategy can ever perform better. What makes uncoded transmission optimal? In this thesis, it is shown that the optimality of uncoded transmission can be understood as the...

To Code or Not to Code, That Is the Question