Detail publikačního výsledku

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

SAMOFALOV, A.; POLÁK, L.

Originální název

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

Anglický název

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

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

image compression, deep learning, autoencoder, AI-generated image, objective metric, image quality

Klíčová slova v angličtině

image compression, deep learning, autoencoder, AI-generated image, objective metric, image quality

Autoři

SAMOFALOV, A.; POLÁK, L.

Rok RIV

2026

Vydáno

12.05.2025

ISBN

979-8-3315-4447-8

Kniha

35th International Conference Radioelektronika (RADIOELEKTRONIKA)

Strany od

1

Strany do

5

Strany počet

5

URL

https://ieeexplore.ieee.org/abstract/document/11008397

BibTex

@inproceedings{BUT197862,
  author="Andrii {Samofalov} and Ladislav {Polák}",
  title="On the Visual Quality of AI and non-AI Images Compressed by Different Autoencoders",
  booktitle="35th International Conference Radioelektronika (RADIOELEKTRONIKA)",
  year="2025",
  pages="1--5",
  doi="10.1109/RADIOELEKTRONIKA65656.2025.11008397",
  isbn="979-8-3315-4447-8",
  url="https://ieeexplore.ieee.org/abstract/document/11008397"
}