Convolutional Neural Networks for No-Reference Image Quality Assessment

Abstract

In this work we describe a Convolutional Neural Network (CNN) to accurately predict image quality without a reference image. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max and min pooling, two fully connected layers and an output node. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating image quality. This approach achieves state of the art performance on the LIVE dataset and shows excellent generalization ability in cross dataset experiments. Further experiments on images with local distortions demonstrate the local quality estimation ability of our CNN, which is rarely reported in previous literature.

RETRACTED ARTICLE: Quality assessment for virtual reality technology based on real scene

“…At present, there are a lot of researches on the objective evaluation of stereo image quality [17][18][19] . Some papers construct the models of human visual system (HVS), which can evaluate the quality of the stereoscopic image [43]. For example, the work [22,24] can evaluate the quality of the stereoscopic image by the combination of the absolute difference of the left and right views on the stereoscopic perception; The other literatures use error calculation (PSNR) as a index to evaluate the quality of the stereoscopic image by simulate multi-channel decomposition characteristics [46,47].…”

A Cascaded Algorithm for Image Quality Assessment and Image Denoising Based on CNN for Image Security and Authorization

“…We train our model on the entire LIVE database and then test model on the TID2008 database. In Table 4, the LCC indicator shows that our algorithm's performance is slightly better than those of other algorithms, similarly to the CNN method in [16]. The SROCC value of our algorithm is very high, which is obviously better than the performance of the other algorithms.…”

“…Input. Previous NR-IQA methods based on deep learning, such as [16,18], consider only the information of grayscale images and ignore the distortion information contained in the color components of the image. Yet, as can be seen in Figure 3, distortion has the most significant effect on the hue component.…”

“…However, there are very few methods of image preprocessing in the field of image quality evaluation, because most preprocessing operations will enhance or reduce the distortion information in the image. The existing no-reference image quality evaluation methods, such as those discussed in the literature [15,16], use local contrast normalization as a preprocessing operation. As Ruderma described in [17], image local normalization has the effect of image decorrelation.…”

Saliency-based deep convolutional neural network for no-reference image quality assessment

“…We first investigate the effect of depth of CNNs for NR-IQA by comparing our proposed ten-layer Deep CNN (DCNN) for NR-IQA with the state-of-the-art CNN architecture proposed by Kang et al (2014). Our results show that the DCNN architecture can deliver a higher accuracy on the LIVE dataset.…”