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Multi-Pooled Inception Features for No-Reference Image Quality Assessment

Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest H-1111, Hungary
Appl. Sci. 2020, 10(6), 2186; https://doi.org/10.3390/app10062186
Received: 5 February 2020 / Revised: 10 March 2020 / Accepted: 18 March 2020 / Published: 23 March 2020
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
Image quality assessment (IQA) is an important element of a broad spectrum of applications ranging from automatic video streaming to display technology. Furthermore, the measurement of image quality requires a balanced investigation of image content and features. Our proposed approach extracts visual features by attaching global average pooling (GAP) layers to multiple Inception modules of on an ImageNet database pretrained convolutional neural network (CNN). In contrast to previous methods, we do not take patches from the input image. Instead, the input image is treated as a whole and is run through a pretrained CNN body to extract resolution-independent, multi-level deep features. As a consequence, our method can be easily generalized to any input image size and pretrained CNNs. Thus, we present a detailed parameter study with respect to the CNN base architectures and the effectiveness of different deep features. We demonstrate that our best proposal—called MultiGAP-NRIQA—is able to outperform the state-of-the-art on three benchmark IQA databases. Furthermore, these results were also confirmed in a cross database test using the LIVE In the Wild Image Quality Challenge database. View Full-Text
Keywords: no-reference image quality assessment; deep learning; convolutional neural networks no-reference image quality assessment; deep learning; convolutional neural networks
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Varga, D. Multi-Pooled Inception Features for No-Reference Image Quality Assessment. Appl. Sci. 2020, 10, 2186.

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