No-reference image quality assessment based on an objective quality database and deep neural networks

Xiazhao Zhang; Peng Yan; Xuanqin Mou

doi:10.1117/12.2536868

18 November 2019 No-reference image quality assessment based on an objective quality database and deep neural networks

Xiazhao Zhang, Peng Yan, Xuanqin Mou

Proceedings Volume 11187, Optoelectronic Imaging and Multimedia Technology VI; 1118708 (2019) https://doi.org/10.1117/12.2536868
Event: SPIE/COS Photonics Asia, 2019, Hangzhou, China

Abstract

Image quality assessment (IQA) has always been an active research topic since the birth of the digital image. Actually, the arrival of deep learning has made IQA more promising. However, most state-of-the-art no-reference (NR) IQA methods require regression training on distorted images or extracted features with subjective image scores, which makes them suffer from insufficient reference image content and training samples with subjectively scoring due to timeconsuming and laborious subjective testing. Furthermore, most convolutional neural networks (CNN)-based methods generally transform original images into patches to accommodate fixed-size input of CNN, which often alter the image's data and introduce noise into the neural network. This paper aims to solve the above problems by adopting new strategies and proposes a novel NRIQA method based on deep CNN. Specifically, first, we obtain image data with diverse image content, multiple image sizes, and reasonable distortion by crawling, filtrating, and degrading numerous publicly licensed high-quality images from the Internet. Then, we score all the images using an excellent full-reference (FR) IQA algorithm, thereby artificially construct a large objective IQA database. Next, we design a deep CNN, which can accept input images of original sizes from our database instead of patches, then we train the model with the FRIQA index as training objective thus propose the opinionunaware( OU) NRIQA method. Finally, the experiment results show that our method achieves excellent performance, which outperforms state-of-the-art OU-NRIQA models and is comparable to most of the traditional opinion-aware NRIQA methods, even some FRIQA methods on standard subjective IQA databases.

Citation Download Citation

Xiazhao Zhang, Peng Yan, and Xuanqin Mou "No-reference image quality assessment based on an objective quality database and deep neural networks", Proc. SPIE 11187, Optoelectronic Imaging and Multimedia Technology VI, 1118708 (18 November 2019); https://doi.org/10.1117/12.2536868

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KEYWORDS

Image quality

Databases

Neural networks

Data modeling

Convolutional neural networks

Digital imaging

Feature extraction

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