Title :
Image Quality Assessment with Degradation on Spatial Structure
Author :
Jinjian Wu ; Weisi Lin ; Guangming Shi
Author_Institution :
Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi´an, China
Abstract :
In this letter, we introduce an improved structural degradation based image quality assessment (IQA) method. Most of the existing structural similarity based IQA metrics mainly consider the spatial contrast degradation but have not fully considered the changes on the spatial distribution of structures. Since the human visual system (HVS) is sensitive to degradations on both spatial contrast and spatial distribution, both factors need to be considered for IQA. In order to measure the structural degradation on spatial distribution, the local binary patterns (LBPs) are first employed to extract structural information. And then, the LBP shift between the reference and distorted images is computed, because noise distorts structural patterns. Finally, the spatial contrast degradation on each pair of LBP shifts is calculated for quality assessment. Experimental results on three large benchmark databases confirm that the proposed IQA method is highly consistent with the subjective perception.
Keywords :
feature extraction; image processing; HVS; IQA method; LBP; human visual system; image distortion; image quality assessment; improved spatial structure degradation; local binary pattern; spatial contrast degradation; spatial contrast distribution; structural information extraction; structural pattern distortion; Degradation; Distortion measurement; Distribution functions; Graphical models; Image edge detection; Image quality; Image quality assessment; local binary patterns; spatial distribution; structural degradation;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2014.2304714