Image Quality Self-adaptive Assessment Based on Visual Salience Distortion

Feng Ming-kun; Zhao Sheng-mei; Xing Chao

doi:10.11999/JEIT141641

Volume 37 Issue 9

Sep. 2015

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Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(9): 2062-2068

Feng Ming-kun, Zhao Sheng-mei, Xing Chao. Image Quality Self-adaptive Assessment Based on Visual Salience Distortion[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2062-2068. doi: 10.11999/JEIT141641

Citation:

Feng Ming-kun, Zhao Sheng-mei, Xing Chao. Image Quality Self-adaptive Assessment Based on Visual Salience Distortion[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2062-2068. doi: 10.11999/JEIT141641

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Image Quality Self-adaptive Assessment Based on Visual Salience Distortion

doi: 10.11999/JEIT141641

1.
2.
(Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications,

Received Date: 2014-12-25
Rev Recd Date: 2015-04-01
Publish Date: 2015-09-19

Abstract

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.
- Image quality assessment,
- Computer vision,
- log-Gabor filter,
- Visual salience,
- Adaptive pooling

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References(27)

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