Large Database Compression Based on Perceived Information

Lossy compression algorithms trade bits for quality, aiming at reducing as much as possible the bitrate needed to represent the original source (or set of sources), while preserving the source quality. In this letter, we propose a novel paradigm of compression algorithms, aimed at minimizing the inf...

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Bibliographic Details
Published inIEEE signal processing letters Vol. 27; pp. 1735 - 1739
Main Authors Maugey, Thomas, Toni, Laura
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects
Algorithms
Compression algorithms
Computer Science
Covariance matrices
Data compression
Entropy
Image coding
Image Processing
Information Theory
large database
Mathematics
Measurement
Optimization
repurposing
sampling
Signal processing algorithms
Repurposing
Sampling
Large database
Data compression
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Summary:Lossy compression algorithms trade bits for quality, aiming at reducing as much as possible the bitrate needed to represent the original source (or set of sources), while preserving the source quality. In this letter, we propose a novel paradigm of compression algorithms, aimed at minimizing the information loss perceived by the final user instead of the actual source quality loss, under compression rate constraints. As main contributions, we first introduce the concept of perceived information (PI), which reflects the information perceived by a given user experiencing a data collection, and which is evaluated as the volume spanned by the sources features in a personalized latent space. We then formalize the rate-PI optimization problem and propose an algorithm to solve this compression problem. Finally, we validate our algorithm against benchmark solutions with simulation results, showing the gain in taking into account users' preferences while also maximizing the perceived information in the feature domain.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2020.3025478