Image quality assessment method in intelligent transportation systems

2007 ◽  
Author(s):  
Bangping Wang ◽  
Jian You ◽  
Yijun Xiao ◽  
Feng Wang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Intelligent Transportation Systems ◽  
Image Quality Assessment ◽  
Intelligent Transportation ◽  
Assessment Method ◽  
Transportation Systems ◽  
Quality Assessment Method

scholarly journals Blind Tone-Mapped Image Quality Assessment Based on Regional Sparse Response and Aesthetics

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 850
Author(s):  
Zhouyan He ◽  
Mei Yu ◽  
Fen Chen ◽  
Zongju Peng ◽  
Haiyong Xu ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Assessment Method ◽  
Quality Assessment Method ◽  
Scene Information ◽  
Detail Loss ◽  
Low Dynamic Range

High dynamic range (HDR) images give a strong disposition to capture all parts of natural scene information due to their wider brightness range than traditional low dynamic range (LDR) images. However, to visualize HDR images on common LDR displays, tone mapping operations (TMOs) are extra required, which inevitably lead to visual quality degradation, especially in the bright and dark regions. To evaluate the performance of different TMOs accurately, this paper proposes a blind tone-mapped image quality assessment method based on regional sparse response and aesthetics (RSRA-BTMI) by considering the influences of detail information and color on the human visual system. Specifically, for the detail loss in a tone-mapped image (TMI), multi-dictionaries are first designed for different brightness regions and whole TMI. Then regional sparse atoms aggregated by local entropy and global reconstruction residuals are presented to characterize the regional and global detail distortion in TMI, respectively. Besides, a few efficient aesthetic features are extracted to measure the color unnaturalness of TMI. Finally, all extracted features are linked with relevant subjective scores to conduct quality regression via random forest. Experimental results on the ESPL-LIVE HDR database demonstrate that the proposed RSRA-BTMI method is superior to the existing state-of-the-art blind TMI quality assessment methods.

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