Non-reference image quality assessment based on deep clustering

Abstract

Image quality assessment (IQA) is an indispensable technique in computer vision and pattern recognition Existing deep IQA methods have achieved remarkable performance. As far as we know, these deep learning-based IQA algorithms lack an adaptive features extraction mechanism toward input images with varying sizes and the stability in avoiding disturbance from data noises and model deviation. To solve these problems, we propose a non-reference IQA method by designing a novel unsupervised deep clustering framework, where a 13-layer network structure is proposed that upgrades the fully-connected layers to produce high-level features with adaptive sizes. Moreover, we add a contracted regular term with a contracted autoencoder into the clustering loss function to form a quality model reflecting the clustering structure. Compared to the other IQA algorithms, our model with simple structure exhibits more stable and robust performance by the initial configuration of network parameters during end-to-end training. The experimental results on the LIVE and CSIP databases have shown that our method not only performs better than the state-of-the-art IQA algorithms, but also has a simpler structure and better adaptability.