Gradient-Related Non-Photorealistic Rendering for High Dynamic Range Images

Author(s):  
Jiajun Lu ◽  
◽  
Fangyan Dong ◽  
Kaoru Hirota

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.

Author(s):  
Annamária R. Várkonyi-Kóczy ◽  
◽  
András Rövid ◽  
Péter Várlaki ◽  

High dynamic range of illumination may cause serious distortions and other problems in viewing and further processing of digital images. In this paper a new fuzzy based tone reproduction pre-processing algorithm is introduced which may help in developing hardly or nonviewable features and content of the images making easier the further processing of it.


2009 ◽  
Vol 35 (2) ◽  
pp. 113-122 ◽  
Author(s):  
Ke-Hu YANG ◽  
Jing JI ◽  
Jian-Jun GUO ◽  
Wen-Sheng YU

2007 ◽  
Vol 40 (10) ◽  
pp. 2641-2655 ◽  
Author(s):  
Guoping Qiu ◽  
Jiang Duan ◽  
Graham D. Finlayson

2018 ◽  
Vol 8 (9) ◽  
pp. 1688 ◽  
Author(s):  
Jinseong Jang ◽  
Hanbyol Jang ◽  
Taejoon Eo ◽  
Kihun Bang ◽  
Dosik Hwang

Image adjustment methods are one of the most widely used post-processing techniques for enhancing image quality and improving the visual preference of the human visual system (HVS). However, the assessment of the adjusted images has been mainly dependent on subjective evaluations. Also, most recently developed automatic assessment methods have mainly focused on evaluating distorted images degraded by compression or noise. The effects of the colorfulness, contrast, and sharpness adjustments on images have been overlooked. In this study, we propose a fully automatic assessment method that evaluates colorfulness-adjusted, contrast-adjusted, and sharpness-adjusted images while considering HVS preferences. The proposed method does not require a reference image and automatically calculates quantitative scores, visual preference, and quality assessment with respect to the level of colorfulness, contrast, and sharpness adjustment. The proposed method evaluates adjusted images based on the features extracted from high dynamic range images, which have higher colorfulness, contrast, and sharpness than that of low dynamic range images. Through experimentation, we demonstrate that our proposed method achieves a higher correlation with subjective evaluations than that of conventional assessment methods.


2014 ◽  
Author(s):  
Bo Xu ◽  
Huachuang Wang ◽  
Mingtao Liang ◽  
Cong Yu ◽  
Jinlong Hu ◽  
...  

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