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Large Database Compression Based on Perceived Information

Maugey, T; Toni, L; (2020) Large Database Compression Based on Perceived Information. IEEE Signal Processing Letters 10.1109/lsp.2020.3025478. (In press). Green open access

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Abstract

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 compressing as much as possible a set of sources, 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.

Type: Article
Title: Large Database Compression Based on Perceived Information
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/lsp.2020.3025478
Publisher version: https://doi.org/10.1109/lsp.2020.3025478
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Covariance matrices , Compression algorithms , Databases , Measurement , Signal processing algorithms , Image coding , Entropy
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10110892
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