An accurate deep convolutional neural networks model for no-reference image quality assessment

The goal of image quality assessment (IQA) is to use computational models to measure the consistency between image quality and subjective evaluations. In recent years, convolutional neural networks (CNNs) have been widely used in image processing community and have achieved performance leaps than non CNNs-based methods. In this work, we describe an accurate deep CNNs model for no-reference IQA. Taking image patches as input, our deep CNNs model achieves an end-to-end method without any handcrafted features and pre-processing procedures that are employed by previous no-reference IQA methods. The proposed model consists of six convolutional layers, two fully connected layers, one max pooling layer and two sum layers. The experimental results verify that our model outperforms the state-of-the-art no-reference IQA methods and most of the full-reference IQA metrics.