Source coding plays a critical role in many modern digital systems. Indeed, as a research area, source coding is more important and fertile than ever, with a wealth of new opportunities and challenges arising from the increasing pervasiveness of multimedia and sensor data in a digital world. This research pursues a set of particularly interesting, novel, and timely directions in this realm, and aims to impact the design of practical systems. There are three complementary aspects to the research: theory, algorithms, and applications.
In the theory area, the emphasis is on problems of distributed source coding. There has been strong growth in interest in such problems, motivated by, for example, the need for resource efficient quantization and compression techniques in networks. The investigation pursues a substantial but largely unexplored aspect of distributed source coding theory: how distortion side information --- i.e., information about the relative importance of the signal components to be coded --- can be exploited in such problems.
In the algorithms area, the focusses on turbo-quantization --- low-complexity iterative source encoding techniques and their implementation. Over the past decade, there has been a tremendous success in developing low-complexity iterative channel decoding algorithms for use with error-correction codes. The corresponding source codes obtained via duality principles have the potential to similarly transform the practice of source coding.
Finally, the research investigates important emerging decision-system applications in which source coding and distortion criteria play an integral role, but in nontraditional ways. Specific examples being pursued include hashing systems for multimedia databases and systems for authenticating multimedia content.
