TextureToMTF: predicting spatial frequency response in the wild

In this work, we propose an no-reference image quality assessment (NR-IQA) approach at a confluence of signal processing and deep learning. We use MTF50 (spatial frequency where modulation transfer function is 50% of its peak value) on slanted edged as a measure for image quality. We propose a comprehensive IQA dataset of images captured through hand-held phone camera in variety of situations with slanted edges around it. The MTF50 values at the slanted edges are then used to garner ground truth values for each patch in the captured images. A convolution neural network is then trained to predict MTF50 values from arbitrary image patches. We present results on the proposed dataset and synthetically generated TID2013 dataset and show state-of-the-art performance for IQA in the wild.

[1]  Joost van de Weijer,et al.  RankIQA: Learning from Rankings for No-Reference Image Quality Assessment , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[2]  Vineet Gandhi,et al.  Document blur detection using edge profile mining , 2016, ICVGIP '16.

[3]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[4]  Junwei Han,et al.  2D-LBP: An Enhanced Local Binary Feature for Texture Image Classification , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Clément Viard,et al.  Quantitative measurement of contrast, texture, color, and noise for digital photography of high dynamic range scenes , 2018, IQSP.

[6]  Peter D. Burns,et al.  Slanted-Edge MTF for Digital Camera and Scanner Analysis , 2000, PICS.

[7]  Paolo Napoletano,et al.  On the use of deep learning for blind image quality assessment , 2016, Signal Image Video Process..

[8]  Vineet Gandhi,et al.  Document Quality Estimation Using Spatial Frequency Response , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  Balasubramanian Raman,et al.  Local neighborhood difference pattern: A new feature descriptor for natural and texture image retrieval , 2018, Multimedia Tools and Applications.

[10]  Zhou Wang,et al.  Image Sharpness Assessment Based on Local Phase Coherence , 2013, IEEE Transactions on Image Processing.

[11]  Jean-Marc Ogier,et al.  Combining Focus Measure Operators to Predict OCR Accuracy in Mobile-Captured Document Images , 2014, 2014 11th IAPR International Workshop on Document Analysis Systems.

[12]  Kwan-Yee Lin,et al.  Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[13]  Dietmar Wueller,et al.  Low light performance of digital still cameras , 2013, Electronic Imaging.

[14]  Le Kang,et al.  A deep learning approach to document image quality assessment , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[15]  Parikshit Sakurikar,et al.  Beyond OCRs for Document Blur Estimation , 2017, 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR).

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

[17]  Shervan Fekri Ershad,et al.  Texture image analysis and texture classification methods - A review , 2019, ArXiv.

[18]  David S. Doermann,et al.  Sharpness estimation for document and scene images , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[19]  Li Xu,et al.  Discriminative Blur Detection Features , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

[21]  Frédéric Guichard,et al.  Measuring texture sharpness of a digital camera , 2009, Electronic Imaging.

[22]  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.

[23]  Lina J. Karam,et al.  A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB) , 2009, IEEE Transactions on Image Processing.

[24]  Min Zhang,et al.  No reference image quality assessment based on local binary pattern statistics , 2013, 2013 Visual Communications and Image Processing (VCIP).

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

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

[27]  Shervan Fekri Ershad,et al.  Color Texture Classification Based on Proposed Impulse-Noise Resistant Color Local Binary Patterns and Significant Points Selection Algorithm , 2017, ArXiv.

[28]  Min Zhang,et al.  Blind Image Quality Assessment Using the Joint Statistics of Generalized Local Binary Pattern , 2015, IEEE Signal Processing Letters.

[29]  Michael T. Postek,et al.  A Kurtosis-Based Statistical Measure for Two-Dimensional Processes and Its Applications to Image Sharpness , 2003 .