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Appl. Sci. 2018, 8(4), 478;

Deep Activation Pooling for Blind Image Quality Assessment

Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, China
College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin 300387, China
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow Scotland G1 1XQ , UK
Author to whom correspondence should be addressed.
Received: 30 January 2018 / Revised: 15 March 2018 / Accepted: 19 March 2018 / Published: 21 March 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
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Driven by the rapid development of digital imaging and network technologies, the opinion-unaware blind image quality assessment (BIQA) method has become an important yet very challenging task. In this paper, we design an effective novel scheme for opinion-unaware BIQA. We first utilize the convolutional maps to select high-contrast patches, and then we utilize these selected patches of pristine images to train a pristine multivariate Gaussian (PMVG) model. In the test stage, each high-contrast patch is fitted by a test MVG (TMVG) model, and the local quality score is obtained by comparing with the PMVG. Finally, we propose the deep activation pooling (DAP) to automatically emphasize the more important scores and suppress the less important ones so as to obtain the overall image quality score. We verify the proposed method on two widely used databases, that is, the computational and subjective image quality (CSIQ) and the laboratory for image and video engineering (LIVE) databases, and the experimental results demonstrate that the proposed method achieves better results than the state-of-the-art methods. View Full-Text
Keywords: deep activation pooling; high-contrast patch selection; image quality assessment deep activation pooling; high-contrast patch selection; image quality assessment

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Zhang, Z.; Wang, H.; Liu, S.; Durrani, T.S. Deep Activation Pooling for Blind Image Quality Assessment. Appl. Sci. 2018, 8, 478.

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