Title :
Local recovery in data compression for general sources
Author :
Arya Mazumdar;Venkat Chandar;Gregory W. Wornell
Author_Institution :
Department of ECE, University of Minnesota, Minneapolis, 55455, USA
fDate :
6/1/2015 12:00:00 AM
Abstract :
Source coding is concerned with optimally compressing data, so that it can be reconstructed up to a specified distortion from its compressed representation. Usually, in fixed-length compression, a sequence of n symbols (from some alphabet) is encoded to a sequence of k symbols (bits). The decoder produces an estimate of the original sequence of n symbols from the encoded bits. The rate-distortion function characterizes the optimal possible rate of compression allowing a given distortion in reconstruction as n grows. This function depends on the source probability distribution. In a locally recoverable decoding, to reconstruct a single symbol, only a few compressed bits are accessed. In this paper we find the limits of local recovery for rates near the rate-distortion function. For a wide set of source distributions, we show that, it is possible to compress within ε of the rate-distortion function such the local recoverability grows as Ω(log(1/ε)); that is, in order to recover one source symbol, at least Ω(log(1/ε)) bits of the compressed symbols are queried. We also show order optimal impossibility results. Similar results are provided for lossless source coding as well.
Keywords :
"Distortion","Decoding","Rate-distortion","Graph theory","Hamming distance","Source coding"
Conference_Titel :
Information Theory (ISIT), 2015 IEEE International Symposium on
Electronic_ISBN :
2157-8117
DOI :
10.1109/ISIT.2015.7283004