Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment

We present a deep neural network-based approach to image quality assessment (IQA). The network is trained end-to-end and comprises ten convolutional layers and five pooling layers for feature extraction, and two fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that: 1) with slight adaptations it can be used in a no-reference (NR) as well as in a full-reference (FR) IQA setting and 2) it allows for joint learning of local quality and local weights, i.e., relative importance of local quality to the global quality estimate, in an unified framework. Our approach is purely data-driven and does not rely on hand-crafted features or other types of prior domain knowledge about the human visual system or image statistics. We evaluate the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the LIVE In the wild image quality challenge database and show superior performance to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation shows a high ability to generalize between different databases, indicating a high robustness of the learned features

Deep Learning frameworks for Image Quality Assessment

Technology is advancing by the arrival of deep learning and it finds huge application in image processing also. Deep learning itself sufficient to perform over all the statistical methods. As a research work, I implemented image quality assessment techniques using deep learning. Here I proposed two full reference image quality assessment algorithms and two no reference image quality algorithms. Among the two algorithms on each method, one is in a supervised manner and other is in an unsupervised manner. First proposed method is the full reference image quality assessment using autoencoder. Existing literature shows that statistical features of pristine images will get distorted in presence of distortion. It will be more advantageous if algorithm itself learns the distortion discriminating features. It will be more complex if the feature length is more. So autoencoder is trained using a large number of pristine images. An autoencoder will give the best lower dimensional representation of the input. It is showed that encoded distance features have good distortion discrimination properties. The proposed algorithm delivers competitive performance over standard databases. If we are giving both reference and distorted images to the model and the model learning itself and gives the scores will reduce the load of extracting features and doing post-processing. But model should be capable one for discriminating the features by itself. Second method which I proposed is a full reference and no reference image quality assessment using deep convolutional neural networks. A network is trained in a supervised manner with subjective scores as targets. The algorithm is performing e�ciently for the distortions that are learned while training the model. Last proposed method is a classiffication based no reference image quality assessment. Distortion level in an image may vary from one region to another region. We may not be able to view distortion in some part but it may be present in other parts. A classiffication model is able to tell whether a given input patch is of low quality or high quality. It is shown that aggregate of the patch quality scores is having a high correlation with the subjective scores

Algorithm Selection for Image Quality Assessment

Subjective perceptual image quality can be assessed in lab studies by human observers. Objective image quality assessment (IQA) refers to algorithms for estimation of the mean subjective quality ratings. Many such methods have been proposed, both for blind IQA in which no original reference image is available as well as for the full-reference case. We compared 8 state-of-the-art algorithms for blind IQA and showed that an oracle, able to predict the best performing method for any given input image, yields a hybrid method that could outperform even the best single existing method by a large margin. In this contribution we address the research question whether established methods to learn such an oracle can improve blind IQA. We applied AutoFolio, a state-of-the-art system that trains an algorithm selector to choose a well-performing algorithm for a given instance. We also trained deep neural networks to predict the best method. Our results did not give a positive answer, algorithm selection did not yield a significant improvement over the single best method. Looking into the results in depth, we observed that the noise in images may have played a role in why our trained classifiers could not predict the oracle. This motivates the consideration of noisiness in IQA methods, a property that has so far not been observed and that opens up several interesting new research questions and applications.Comment: Presented at the Seventh Workshop on COnfiguration and SElection of ALgorithms (COSEAL), Potsdam, Germany, August 26--27, 201

Target-adaptive CNN-based pansharpening

We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network which trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality which ensures a very good performance also in the presence of a mismatch w.r.t. the training set, and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and high-quality CNN-based pansharpening of their own target images on general-purpose hardware