On the Visual Quality of AI and non-AI Images Compressed by Different Autoencoders
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0197862" target="_blank" >RIV/00216305:26220/26:0197862 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/abstract/document/11008397" target="_blank" >https://ieeexplore.ieee.org/abstract/document/11008397</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/RADIOELEKTRONIKA65656.2025.11008397" target="_blank" >10.1109/RADIOELEKTRONIKA65656.2025.11008397</a>
Alternative languages
Result language
angličtina
Original language name
On the Visual Quality of AI and non-AI Images Compressed by Different Autoencoders
Original language description
Image compression using deep learning (DL) tech- niques is an emerging and rapidly evolving field. This approach has the potential to enhance image compression by learning complex patterns and representations from data, enabling higher compression ratios while maintaining high image quality. Unlike conventional compression methods, which rely on predefined algorithms, DL models can adapt and optimize compression based on the specific content of an image. This paper pro vides a comparison-based study of the two autoencoder models (dense and convolutional), commonly used in DL models for image compression. The comparison is based on objective metrics applied to human-made images from a publicly available database and AI-generated images to evaluate the quality of compressed images. The results obtained show that the autoencoders differ in terms of the visual quality of the reconstructed images.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20202 - Communication engineering and systems
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
35th International Conference Radioelektronika (RADIOELEKTRONIKA)
ISBN
979-8-3315-4447-8
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
1-5
Publisher name
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Place of publication
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Event location
Hnanice
Event date
May 12, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001509603700023