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