Deep Convolutional Neural Models for Picture Quality Prediction

Convolutional neural networks (CNNs) have been shown to deliver standout performance in a wide variety of visual information processing applications. However, this rapidly developing technology has not yet been applied with systematic energy to the problem of picture quality prediction, primarily because of limitations imposed by a lack of adequate ground truth human subjective data. This situation has begun to change with the development of promising data-gathering methods that are driving new approaches to deep learning-based perceptual picture quality prediction. Here we assay progress in this rapidly evolving field, focussing in particular on new ways to collect large quantities of ground truth data, and on recent CNN-based picture quality prediction models that deliver excellent results on a large real-world picture quality database.

[1]  Lai-Man Po,et al.  No-reference image quality assessment with shearlet transform and deep neural networks , 2015, Neurocomputing.

[2]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  David S. Doermann,et al.  Unsupervised feature learning framework for no-reference image quality assessment , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Xuelong Li,et al.  Blind Image Quality Assessment via Deep Learning , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Nikolay N. Ponomarenko,et al.  Image database TID2013: Peculiarities, results and perspectives , 2015, Signal Process. Image Commun..

[7]  Fei Gao,et al.  DeepSim: Deep similarity for image quality assessment , 2017, Neurocomputing.

[8]  Jongyoo Kim,et al.  Deep CNN-Based Blind Image Quality Predictor , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Eric C. Larson,et al.  Most apparent distortion: full-reference image quality assessment and the role of strategy , 2010, J. Electronic Imaging.

[10]  Nanning Zheng,et al.  Image Quality Assessment Using Similar Scene as Reference , 2016, ECCV.

[11]  Alan C. Bovik,et al.  Objective quality assessment of multiply distorted images , 2012, 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[12]  Sanghoon Lee,et al.  Fully Deep Blind Image Quality Predictor , 2017, IEEE Journal of Selected Topics in Signal Processing.

[13]  Alan C. Bovik,et al.  Massive Online Crowdsourced Study of Subjective and Objective Picture Quality , 2015, IEEE Transactions on Image Processing.

[14]  Alan C. Bovik,et al.  Automatic Prediction of Perceptual Image and Video Quality , 2013, Proceedings of the IEEE.

[15]  D. Heeger.  Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.

[16]  Alan C. Bovik,et al.  Image information and visual quality , 2006, IEEE Trans. Image Process..

[17]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[18]  Lai-Man Po,et al.  No-reference image quality assessment with deep convolutional neural networks , 2016, 2016 IEEE International Conference on Digital Signal Processing (DSP).

[19]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[20]  Yi Li,et al.  Convolutional Neural Networks for No-Reference Image Quality Assessment , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Sanghoon Lee,et al.  Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Honglak Lee,et al.  Sparse deep belief net model for visual area V2 , 2007, NIPS.

[23]  Alan C. Bovik,et al.  Perceptual quality prediction on authentically distorted images using a bag of features approach , 2016, Journal of vision.

[24]  Sebastian Bosse,et al.  A deep neural network for image quality assessment , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[25]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Alan C. Bovik,et al.  Experiments in segmenting texton patterns using localized spatial filters , 1989, Pattern Recognit..

[27]  Ashish Kapoor,et al.  Learning a blind measure of perceptual image quality , 2011, CVPR 2011.

[28]  Alan C. Bovik,et al.  No-Reference Image Quality Assessment in the Spatial Domain , 2012, IEEE Transactions on Image Processing.

[29]  Alan C. Bovik,et al.  A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms , 2006, IEEE Transactions on Image Processing.