Quality comparison of 360° 8K images compressed by conventional and deep learning algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148052" target="_blank" >RIV/00216305:26220/23:PU148052 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10109066" target="_blank" >https://ieeexplore.ieee.org/document/10109066</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/RADIOELEKTRONIKA57919.2023.10109066" target="_blank" >10.1109/RADIOELEKTRONIKA57919.2023.10109066</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Quality comparison of 360° 8K images compressed by conventional and deep learning algorithms

Popis výsledku v původním jazyce

Due to the accessibility of virtual reality in recent years, there has been a great interest in producing and streaming omnidirectional (360° field of view) high resolution images and videos. Since both high resolution and high quality are demanding for the storage and distribution of such content, the use of advanced compression methods is a key factor in achieving this goal. This paper provides an objective comparison of conventional image compression codecs (JPEG, JPEG XL, HEIC, AVIF, VVC Intra) and deep learning image compression algorithms with a JPEG AI framework recommendation. The visual quality evaluation is based on ten images from publicly available databases compressed to predetermined bit rates. Six full reference objective metrics (WS-PSNR, MS-SSIM, VIFp, FSIMc, GMSD, VMAF) are used to evaluate the visual quality of the compressed images. Modern image compression codecs outperform the oldest and most widely used codec JPEG in terms of bandwidth reduction but require more processing power and system resources.

Název v anglickém jazyce

Quality comparison of 360° 8K images compressed by conventional and deep learning algorithms

Popis výsledku anglicky

Due to the accessibility of virtual reality in recent years, there has been a great interest in producing and streaming omnidirectional (360° field of view) high resolution images and videos. Since both high resolution and high quality are demanding for the storage and distribution of such content, the use of advanced compression methods is a key factor in achieving this goal. This paper provides an objective comparison of conventional image compression codecs (JPEG, JPEG XL, HEIC, AVIF, VVC Intra) and deep learning image compression algorithms with a JPEG AI framework recommendation. The visual quality evaluation is based on ten images from publicly available databases compressed to predetermined bit rates. Six full reference objective metrics (WS-PSNR, MS-SSIM, VIFp, FSIMc, GMSD, VMAF) are used to evaluate the visual quality of the compressed images. Modern image compression codecs outperform the oldest and most widely used codec JPEG in terms of bandwidth reduction but require more processing power and system resources.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

33rd International Conference Radioelektronika

ISBN

979-8-3503-9834-2

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

„“-„“

Název nakladatele

Neuveden

Místo vydání

Pardubice

Místo konání akce

Pardubice

Datum konání akce

19. 4. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000990505700039