On the Visual Quality of AI and non-AI Images Compressed by Different Autoencoders

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

On the Visual Quality of AI and non-AI Images Compressed by Different Autoencoders

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

On the Visual Quality of AI and non-AI Images Compressed by Different Autoencoders

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

35th International Conference Radioelektronika (RADIOELEKTRONIKA)

ISBN

979-8-3315-4447-8

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

—

Místo vydání

—

Místo konání akce

Hnanice

Datum konání akce

12. 5. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001509603700023