Noise-Insensitive Contrast Enhancement for Rendering High-Dynamic-Range Images

2010 ◽  
Author(s):  
Hsueh-Yi Sean Lin
Keyword(s):  
Contrast Enhancement ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Range Images ◽  
High Dynamic

High Dynamic Range Images and Tone Mapping Operator

2009 ◽  
Vol 35 (2) ◽  
pp. 113-122 ◽  
Author(s):  
Ke-Hu YANG ◽  
Jing JI ◽  
Jian-Jun GUO ◽  
Wen-Sheng YU
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Tone Mapping ◽  
Range Images ◽  
High Dynamic

scholarly journals Learning to display high dynamic range images

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

scholarly journals No-reference Automatic Quality Assessment for Colorfulness-Adjusted, Contrast-Adjusted, and Sharpness-Adjusted Images Using High-Dynamic-Range-Derived Features

2018 ◽  
Vol 8 (9) ◽  
pp. 1688 ◽  
Author(s):  
Jinseong Jang ◽  
Hanbyol Jang ◽  
Taejoon Eo ◽  
Kihun Bang ◽  
Dosik Hwang
Keyword(s):  
Quality Assessment ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Assessment Methods ◽  
Reference Image ◽  
Range Images ◽  
Automatic Assessment ◽  
Visual Preference ◽  
Subjective Evaluations ◽  
High Dynamic

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.

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