z-logo
open-access-imgOpen Access
Gradient-Related Non-Photorealistic Rendering for High Dynamic Range Images
Author(s) -
Jiajun Lu,
Fangyan Dong,
Kaoru Hirota
Publication year - 2013
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2013.p0628
Subject(s) - rendering (computer graphics) , high dynamic range , computer science , computer vision , dither , artificial intelligence , computer graphics (images) , luminance , high dynamic range imaging , dynamic range , noise shaping
A non-photorealistic rendering (NPR) method based on elements, usually strokes, is proposed for rendering high dynamic range (HDR) images to mimic the visual perception of human artists and designers. It enables strokes generated in the rendering process to be placed accurately on account of improvements in computing gradient values especially in regions having particularly high or low luminance. Experimental results using a designed pattern show that angles of gradient values obtained from HDR images have a reduction in averaged error of up to 57.5% in comparison to that of conventional digital images. A partial experiment on incorporating HDR images into other NPR styles, such as dithering, shows the wide compatibility of HDR images in providing source information for NPR processes.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom