2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | O.1.5 | ||
| Paper Title | $O(\log \log n)$ Worst-Case Local Decoding and Update Efficiency for Data Compression | ||
| Authors | Shashank Vatedka, Indian Institute of Technology Hyderabad, India; Venkat Chandar, DE Shaw, United States; Aslan Tchamkerten, Telecom Paris, France | ||
| Session | O.1: Data Compression | ||
| Presentation | Lecture | ||
| Track | Source Coding | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | This paper addresses the problem of data compression with local decoding and local update. A compression scheme has worst-case local decoding $ d_{wc} $ if we can recover any bit of the raw file by probing at most $ d_{wc} $ bits of the compressed sequence, and has update efficiency of $u_{wc} $ if a single bit of the raw file can be updated by modifying at most $ u_{wc} $ bits of the compressed sequence. This article provides an entropy-achieving compression scheme for memoryless sources that simultaneously achieves $ O(\log\log n) $ local decoding and update efficiency. Key to this achievability result is a novel succinct data structure for sparse sequences which allows efficient local decoding and local update. Under general assumptions on the local decode and update algorithms, a converse result shows that the maximum of $ d_{wc} $ and $ u_{wc} $ must grow as $ \Omega(\log\log n) $. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
