Color Quality Comparison in Spectrally (Un)Correlated Random Field Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F24%3A00587782" target="_blank" >RIV/67985556:_____/24:00587782 - isvavai.cz</a>

Výsledek na webu

<a href="http://wscg.zcu.cz/WSCG2024/CSRN-2024/C05-2024.pdf" target="_blank" >http://wscg.zcu.cz/WSCG2024/CSRN-2024/C05-2024.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.24132/CSRN.3401.21" target="_blank" >10.24132/CSRN.3401.21</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Color Quality Comparison in Spectrally (Un)Correlated Random Field Models

Popis výsledku v původním jazyce

We inspect the ability to reproduce spectral (color) composition in random field-based texture models, test when it can neglect spectral correlation, and simplify these random models without visibly depreciating their visual quality. These probabilistic models present essential two or three-dimensional factors for modeling seven-dimensional Bidirectional Texture Function (BTF) - the most advanced representation currently used in real-world material visual properties modeling. They can seamlessly approximate original measured massive data and extend them to an arbitrary size or simulate unmeasured ones. Using extensive test data sets and a small set of setup control parameters, these models reach a vast compression ratio while maintaining the visual quality of measurements, and thus, they are the only viable alternative for BTF practical usage.

Název v anglickém jazyce

Color Quality Comparison in Spectrally (Un)Correlated Random Field Models

Popis výsledku anglicky

We inspect the ability to reproduce spectral (color) composition in random field-based texture models, test when it can neglect spectral correlation, and simplify these random models without visibly depreciating their visual quality. These probabilistic models present essential two or three-dimensional factors for modeling seven-dimensional Bidirectional Texture Function (BTF) - the most advanced representation currently used in real-world material visual properties modeling. They can seamlessly approximate original measured massive data and extend them to an arbitrary size or simulate unmeasured ones. Using extensive test data sets and a small set of setup control parameters, these models reach a vast compression ratio while maintaining the visual quality of measurements, and thus, they are the only viable alternative for BTF practical usage.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Computer Science Research Notes

ISSN

2464-4617

e-ISSN

2464-4625

Svazek periodika

3401

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

10

Strana od-do

197-206

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85211110709