0574371 20240402214252.020230818235959.9 10.1109/ICASSPW59220.2023.10193606 5 s. P cav_un_epca*0574370979-8-3503-0262-2PiscatawayIEEE2023 Texture quality criteria Spearman correlation Human quality ranking Texture quality benchmark 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. cav_un_auth*0453250 Shaih N. IN http://library.utia.cas.cz/separaty/2023/RO/haindl-0574371.pdf GA19-12340S GA ČR CZ cav_un_auth*0376011 Visual scene recognition or modeling predominantly uses visual textures representing an object's material properties. However, the single material texture varies in scale and illumination angles due to mapping an object's shape. We present a comparative study of thirteen possible texture quality criteria and show the superior performance of two multispectral measures derived from the Markovian descriptive model. cav_un_auth*0453251 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2023 /48./ 20230604 20230610 Rhodes GR IN 20000 20200 20205 2024 2 PR RVO:67985556 https://hdl.handle.net/11104/0344727 S 7101 2023 SCOPUS PUBMED WOS cav_un_epca*0574370 Proceedings of the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW 2023) IEEE 2023 Piscataway 979-8-3503-0262-2