Image quality assessment (IQA) is a fundamental yet constantly developing task for computer vision and image processing. Most IQA evaluation mechanisms are based on the pertinence of subjective and objective estimation. Each image distortion type has its own property correlated with human perception. However, this intrinsic property may not be fully exploited by existing IQA methods. In this paper, we make two main contributions to the IQA field. First, a novel IQA method is developed based on a local linear model that examines the distortion between the reference and the distorted images for better alignment with human visual experience. Second, a distortion-specific compensation strategy is proposed to offset the negative effect on IQA modeling caused by different image distortion types. These score offsets are learned from several known distortion types. Furthermore, for an image with an unknown distortion type, a convolutional neural network-based method is proposed to compute the score offset automatically. Finally, an integrated IQA metric is proposed by combining the aforementioned two ideas. Extensive experiments are performed to verify the proposed IQA metric, which demonstrate that the local linear model is useful in human perception modeling, especially for individual image distortion, and the overall IQA method outperforms several state-of-the-art IQA approaches.

The past decade has witnessed a growing development of Image Quality Assessment (IQA) techniques. However, the researches of IQA with multiple distortion types are still limited especially on blind image quality assessment methods. In this paper, a Convolutional Neural Network (CNN) based method is proposed to predict the quality of multiply distorted images without references. Inspired by the early human visual model, the proposed CNN based method combines feature learning and regression for estimating the quality of multiply distorted images. The proposed network consists of one convolutional layer, one pooling layer with max and average pooling, two full connection layers and one softmax classification layer. With this network structure, the relationship between the accuracy of CNN and the prediction monotonicity of IQA is explored. Experimental results on the newly released LIVE multiply distorted image quality database verify the effectiveness of the proposed CNN based method.

A number of image quality assessment (IQA) metrics have been designed in recent years for natural images, leading to a desire to develop IQA approaches for screen content image which is composed of textual as well as pictorial regions and exhibits different visual characteristics from the natural image. In this work, a no reference IQA metric based on convolutional neural network (CNN) is proposed for screen content image, which fuses the quality scores of textual and pictorial image patches by taking the differences of their visual features into accounts. Experimental results on the benchmark screen image quality assessment database (SIQAD) verify the effectiveness of the proposed CNN based approach as compared with several state-of-the-art IQA metrics.

The past decades have witnessed a growing development of image quality assessment (IQA). However, there is still much room to improve the IQA performance, especially for the no reference IQA problem. In this paper, a convolutional neural network (CNN) based approach is designed to predict the distortion type of an image and assess its quality without original reference. With the proposed CNN approach, an image is divided into patches and a selective weighted average method is designed to fuse the image quality score from the image patches with the aid of recognized distortion type. Experimental results on the benchmark LIVE image quality database verify the effectiveness of the proposed CNN based approach as compared with several state-of-the-art full reference and no reference image quality metrics.