Source Coding with Distortion through Graph Coloring (Vishal Doshi, Devavrat Shah, and Muriel Médard) : This paper looked at the rate-distortion function for reconstructing a function f(X,Y) with X at the encoder and Y as side information at the decoder. One can think of this as a “functional” Wyner-Ziv, or in some cases as a noisy Wyner-Ziv. In the lossless setting, the reconstruction function can be found using graph entropy, and minimum-entropy graph coloring turns out to be a way of obtaining a solution. For the lossy problem, they find a similar graph coloring scheme but can’t get a single-letter expression in all cases. What is interesting to me is the optimality of “preprocess + Slepian-Wolf,” which is similar in spirit to the work done by Wang, Wagner, Viswanath and others for Gaussian multiterminal source coding problems.
Compound conditional source coding, Slepian-Wolf list decoding, and applications to media coding (Stark C. Draper and Emin Martinian) : The main motivation here was that many multimedia applications (such as video coding) may more fruitfully be thought of as compound source coding problems with an unknown side information at the decoder. In this setting, we can imagine the side information as one of P different predictors of the source X available at the encoder. The encoder can use the different predictors to list decode the source message and send conditional list-disambiguation information in addition to the source encoding. It’s a neat scheme that seems quite related to some of my thesis work on list decoding for AVCs.
Correlated Sources over a Noisy Channel: Cooperation in the Wideband Limit (Chulhan Lee and Sriram Vishwanath) : I have to admit I didn’t fully get this talk, but it looked at wideband distributed source coding using “highly correlated sources.” They propose a modified PPM scheme which can exploit the correlation as if the encoding is joint with small bit error rate (but possibly larger block-error rate). What was unclear to me was why the modified error criterion was necessary, but it seems to be an artifact of the proposed scheme. The algorithm requires a sliding window decoder whose analysis seems a bit tricky.
Joint Universal Lossy Coding and Identification of Stationary Mixing Sources (Maxim Raginsky) : What is the loss in estimating the parameter of a source and doing universal lossy source coding? By using a competitive optimality framework and a Lagrangian formulation to trade off the parameter error and source distortion, Raginsky can bound the loss in performance. This falls into the category of the O(log n/sqrt(n)) results that I don’t know much about, but I will probably take a look at the full paper to get a better idea of how the codes work. He uses some ideas of VC dimension from learning theory, which I know a little about, so hopefully it will not be too hard going…
The source coding game with a cheating switcher (Hari Palaiyanur, Cheng Chang, Anant Sahai) : This is an extension of Berger’s source coding game, in which a switcher switches between k memoryless sources and you have to make a rate distortion code that can handle the worst case behavior of the switcher. Before, the switcher could not see the source outputs first. Here, he can (hence “cheating”). The main point is to figure out which iid distributions the switcher can emulate, and the worst one in that set gives the bound. The rest is a union bound over types and doesn’t affect the rate.