基于视觉显著失真度的图像质量自适应评价方法

doi:10.11999/JEIT141641

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Abstract

The Structural SIMilarity (SSIM) algorithm of image quality assessment does not take into account the characteristics of multi-channel resolutions of human vision, it is also not consistent with subjective human evaluation for high level distortions. A Visual Salience Adaptive Pooling (VSAP) strategy of image quality assessment is proposed based on visual multi-scale and multi-orientation of log-Gabor transformation. Firstly, the visual characteristics of image on the high, medium, and low frequency are extracted by the log-Gabor transformation. Then the visual similarity scores based on visual scales and visual orientations of log-Gabor are calculated, accordingly, the visual distortion levels of image are calculated iteratively with the visual multi- resolution threshold. Finally, a strategy of image quality assessment is proposed with adaptive pooling similarity scores to distortion scores. The experimental results show that objective assessments of VSAP for different types of distortion hold higher correlation with subjective assessment. More importantly, the overall assessment performance of the Spearman Rank-Order Correlation Coefficient (SROCC), Correlation Coefficient (CC) and Root Mean Square Error (RMSE) for different levels of distortion is more consistent with subjective scores and superior to other methods.

Key words： Image quality assessment Computer vision log-Gabor filter Visual salience Adaptive pooling

Received: 25 December 2014 Published: 11 June 2015

PACS:

TN911.73

Corresponding Authors: Feng Ming-kun E-mail: 104027@zust.edu.cn

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	Feng Ming-kun
	Zhao Sheng-mei
	Xing Chao

Cite this article:

Feng Ming-kun,Zhao Sheng-mei,Xing Chao. Image Quality Self-adaptive Assessment Based on Visual Salience Distortion[J]. JEIT, 2015, 37(9): 2062-2068.

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http://jeit.ie.ac.cn/EN/ 10.11999/JEIT141641 OR http://jeit.ie.ac.cn/EN/Y2015/V37/I9/2062

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