Representing information with computational resource bounds

Author

Sow, Daby ; Eleftheriadis, Alexandros

Author_Institution

Dept. of Electr. Eng., Columbia Univ., New York, NY, USA

Volume

1

fYear

1998

fDate

1-4 Nov. 1998

Firstpage

452

Abstract

A general framework for data compression, is which computational resource bounds are introduced at both the encoding and decoding end, is presented. We move away from Shannon´s (1948) traditional communication system by introducing some structure at the decoder and model it by a Turing machine with finite computational resources. Information is measured using the resource bounded Kolmogorov (1965) complexity. In this setting, we investigate the design of efficient lossy encoders.

Keywords

Turing machines; codecs; computational complexity; data compression; decoding; encoding; signal representation; Shannon´s communication system; Turing machine; codec; computational resource bounds; data compression; decoder; decoding; efficient lossy encoder design; encoding; finite computational resources; information representation; resource bounded Kolmogorov complexity; Channel capacity; Data compression; Data engineering; Decoding; Entropy; Information theory; Length measurement; Stochastic processes; Stochastic resonance; Turing machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems & Computers, 1998. Conference Record of the Thirty-Second Asilomar Conference on

Conference_Location

Pacific Grove, CA, USA

ISSN

1058-6393

Print_ISBN

0-7803-5148-7

Type

conf

DOI

10.1109/ACSSC.1998.750904

Filename

750904