0587782 20250317091714.820240722235959.9 85211110709 10.24132/CSRN.3401.21 10 s. E cav_un_epca*05737112464-461734011 (2024)197-206 Texture Analysis Texture Synthesis Texture Color Quality Comparison Texture Modeling BTF cav_un_auth*0283206 Havlíček Michal UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101093 Haindl Michal UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition A Ústav teorie informace a automatizace AV ČR, v. v. i. https://library.utia.cas.cz/separaty/2024/RO/haindl-0587782.pdf http://wscg.zcu.cz/WSCG2024/CSRN-2024/C05-2024.pdf 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. cav_un_auth*0470087 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2024 20240515 20240519 Plzen CZ SCOPUS BD 20000 20200 20205 2025 2 RVO:67985556 https://hdl.handle.net/11104/0355030 S C 0.121 Q4 2024 85211110709 SCOPUS PUBMED WOS cav_un_epca*0573711 Computer Science Research Notes 3401 1 2024 197 206 2464-4617 2464-4625