A Rate-Distortion Framework for Characterizing Semantic Information

A rate-distortion problem motivated by the consideration of semantic information is formulated and solved. The starting point is to model an information source as a pair consisting of an intrinsic state which is not observable, corresponding to the semantic aspect of the source, and an extrinsic obs...

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Bibliographic Details
Published in2021 IEEE International Symposium on Information Theory (ISIT) pp. 2894 - 2899
Main Authors Liu, Jiakun, Zhang, Wenyi, Poor, H. Vincent
Format Conference Proceeding
LanguageEnglish
Published IEEE 12.07.2021
Subjects
Computational modeling
Distortion
Numerical models
Rate-distortion
Semantics
Source coding
Training data
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Summary:A rate-distortion problem motivated by the consideration of semantic information is formulated and solved. The starting point is to model an information source as a pair consisting of an intrinsic state which is not observable, corresponding to the semantic aspect of the source, and an extrinsic observation which is subject to lossy source coding. The proposed rate-distortion problem seeks a description of the information source, via encoding the extrinsic observation, under two distortion constraints, one for the intrinsic state and the other for the extrinsic observation. The corresponding state-observation rate-distortion function is obtained, and a few case studies of Gaussian intrinsic state estimation and binary intrinsic state classification are studied.
DOI:10.1109/ISIT45174.2021.9518240