Comprehensive image quality assessment via predicting the distribution of opinion score

Image quality assessment is a challenge problem in image processing area. Previous works usually predict the mean opinion score (MOS) to evaluate image quality. However, it is found that the distribution of opinion scores provides richer and more precise semantics information. Therefore, in this work, we focus on the distribution of opinion scores (DOS) and aims to comprehensively evaluate image quality via automatically predicting DOS. Specifically, we first extract image features via convolutional neural network and then adopt the label distribution support vector regressor (LDSVR) algorithm to predict score distribution. To the best of our knowledge, we are the first to introduce label distribution learning approach for image quality assessment. Extensive experiments have been carried out and validate that the proposed algorithm can well predict the DOS and provide a comprehensive assessment to image quality.

[1]  Zi Huang,et al.  Exploring Consistent Preferences: Discrete Hashing with Pair-Exemplar for Scalable Landmark Search , 2017, ACM Multimedia.

[2]  Nikolay N. Ponomarenko,et al.  Color image database TID2013: Peculiarities and preliminary results , 2013, European Workshop on Visual Information Processing (EUVIP).

[3]  Xian-Sheng Hua,et al.  A transductive multi-label learning approach for video concept detection , 2011, Pattern Recognit..

[4]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  John D. Lafferty,et al.  Inducing Features of Random Fields , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Li Fei-Fei,et al.  Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.

[7]  Christophe Charrier,et al.  Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain , 2012, IEEE Transactions on Image Processing.

[8]  Meng Wang,et al.  Oracle in Image Search: A Content-Based Approach to Performance Prediction , 2012, TOIS.

[9]  Fernando Pérez-Cruz,et al.  SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems , 2004, IEEE Transactions on Signal Processing.

[10]  Alexandre Bernardino,et al.  Matrix Completion for Multi-label Image Classification , 2011, NIPS.

[11]  Xin Geng,et al.  Label Distribution Learning , 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

[12]  Trevor Darrell,et al.  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.

[13]  Leon A. Gatys,et al.  Image Style Transfer Using Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Andrea Vedaldi,et al.  MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.

[15]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[16]  Lei Zhu,et al.  Unsupervised Visual Hashing with Semantic Assistant for Content-Based Image Retrieval , 2017, IEEE Transactions on Knowledge and Data Engineering.

[17]  Jian Yang,et al.  Coupled-learning convolutional neural networks for object recognition , 2017, Multimedia Tools and Applications.

[18]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[19]  Ling Shao,et al.  Dynamic Multi-View Hashing for Online Image Retrieval , 2017, IJCAI.

[20]  Zhi-Hua Zhou,et al.  Facial Age Estimation by Learning from Label Distributions , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Ke Lu,et al.  Transfer Independently Together: A Generalized Framework for Domain Adaptation , 2019, IEEE Transactions on Cybernetics.

[22]  Jan Kautz,et al.  Bitmap Movement Detection: HDR for Dynamic Scenes , 2010, 2010 Conference on Visual Media Production.

[23]  Fernando Pérez-Cruz,et al.  Fast Training of Support Vector Classifiers , 2000, NIPS.

[24]  Xuan Wang,et al.  Quality biased multimedia data retrieval in microblogs , 2016, J. Vis. Commun. Image Represent..

[25]  Dani Lischinski,et al.  Gradient Domain High Dynamic Range Compression , 2023 .

[26]  Heng Tao Shen,et al.  Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[27]  Alan C. Bovik,et al.  No-Reference Quality Assessment of Tone-Mapped HDR Pictures , 2017, IEEE Transactions on Image Processing.

[28]  Meng Wang,et al.  A Framework of Joint Low-Rank and Sparse Regression for Image Memorability Prediction , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

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

[30]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

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

[32]  Leon A. Gatys,et al.  Texture Synthesis Using Convolutional Neural Networks , 2015, NIPS.

[33]  Timothy N. Rubin,et al.  Statistical topic models for multi-label document classification , 2011, Machine Learning.

[34]  Yu Liu,et al.  CNN-RNN: a large-scale hierarchical image classification framework , 2018, Multimedia Tools and Applications.

[35]  Leon A. Gatys,et al.  A Neural Algorithm of Artistic Style , 2015, ArXiv.

[36]  Haitao Huang,et al.  Abstractive text summarization using LSTM-CNN based deep learning , 2018, Multimedia Tools and Applications.

[37]  Hong Liu,et al.  Off-the-shelf CNN features for 3D object retrieval , 2017, Multimedia Tools and Applications.

[38]  Emilio Arnieri,et al.  Support Vector Regression Machines to Evaluate Resonant Frequency of Elliptic Substrate Integrate Waveguide Resonators , 2008 .

[39]  Xin Geng,et al.  Pre-release Prediction of Crowd Opinion on Movies by Label Distribution Learning , 2015, IJCAI.

[40]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[41]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[42]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[43]  Subhasis Chaudhuri,et al.  Bilateral Filter Based Compositing for Variable Exposure Photography , 2009, Eurographics.

[44]  Christine D. Piatko,et al.  A visibility matching tone reproduction operator for high dynamic range scenes , 1997 .

[45]  Tat-Seng Chua,et al.  Quality Matters: Assessing cQA Pair Quality via Transductive Multi-View Learning , 2018, IJCAI.

[46]  Gustavo de Veciana,et al.  An information fidelity criterion for image quality assessment using natural scene statistics , 2005, IEEE Transactions on Image Processing.

[47]  Alan C. Bovik,et al.  Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality , 2011, IEEE Transactions on Image Processing.

[48]  Raimondo Schettini,et al.  How to assess image quality within a workflow chain: an overview , 2014, International Journal on Digital Libraries.