Assessing Personally Perceived Image Quality via Image Features and Collaborative Filtering

During the past few years, different methods for optimizing the camera settings and post-processing techniques to improve the subjective quality of consumer photos have been studied extensively. However, most of the research in the prior art has focused on finding the optimal method for an average user. Since there is large deviation in personal opinions and aesthetic standards, the next challenge is to find the settings and post-processing techniques that fit to the individual users’ personal taste. In this study, we aim to predict the personally perceived image quality by combining classical image feature analysis and collaboration filtering approach known from the recommendation systems. The experimental results for the proposed method show promising results. As a practical application, our work can be used for personalizing the camera settings or post-processing parameters for different users and images.

[1]  Mohamed A. Deriche,et al.  A critical look to some contrast enhancement evaluation measures , 2015, 2015 Colour and Visual Computing Symposium (CVCS).

[2]  Jari Korhonen.  Predicting personal preferences in subjective video quality assessment , 2017, 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX).

[3]  Weisi Lin,et al.  The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement , 2016, IEEE Transactions on Cybernetics.

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

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

[6]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[7]  Alan C. Bovik,et al.  Making a “Completely Blind” Image Quality Analyzer , 2013, IEEE Signal Processing Letters.

[8]  Dit-Yan Yeung,et al.  Collaborative Deep Learning for Recommender Systems , 2014, KDD.

[9]  Mohamed A. Deriche,et al.  Towards the design of a consistent image contrast enhancement evaluation measure , 2017, Signal Process. Image Commun..

[10]  Christophe Charrier,et al.  Blind Prediction of Natural Video Quality , 2014, IEEE Transactions on Image Processing.

[11]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[12]  Zhou Wang,et al.  A Patch-Structure Representation Method for Quality Assessment of Contrast Changed Images , 2015, IEEE Signal Processing Letters.

[13]  Weisi Lin,et al.  No-Reference and Robust Image Sharpness Evaluation Based on Multiscale Spatial and Spectral Features , 2017, IEEE Transactions on Multimedia.

[14]  Winston H. Hsu,et al.  Photo Filter Recommendation by Category-Aware Aesthetic Learning , 2016, IEEE Transactions on Multimedia.

[15]  Xu Wang,et al.  Subjective Assessment of Post-Processing Methods for Low Light Consumer Photos , 2018, 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX).

[16]  Vladimir V. Lukin,et al.  Statistical Evaluation of Visual Quality Metrics for Image Denoising , 2017, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[17]  Martha Larson,et al.  Collaborative Filtering beyond the User-Item Matrix , 2014, ACM Comput. Surv..

[18]  Stefan Harmeling,et al.  Image denoising: Can plain neural networks compete with BM3D? , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Nicolai Petkov,et al.  Artistic Edge and Corner Enhancing Smoothing , 2007, IEEE Transactions on Image Processing.

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

[21]  Karel Fliegel,et al.  Quality Assessment of Sharpened Images: Challenges, Methodology, and Objective Metrics , 2017, IEEE Transactions on Image Processing.

[22]  Mohamed A. Deriche,et al.  A comprehensive performance evaluation of objective quality metrics for contrast enhancement techniques , 2016, 2016 6th European Workshop on Visual Information Processing (EUVIP).

[23]  Nicholas Jing Yuan,et al.  Collaborative Knowledge Base Embedding for Recommender Systems , 2016, KDD.

[24]  Wei Yu,et al.  On learning optimized reaction diffusion processes for effective image restoration , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Yonghong Tian,et al.  Quality Assessment for Comparing Image Enhancement Algorithms , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Azeddine Beghdadi,et al.  Contrast Enhancement Evaluation Database (CEED2016) , 2017 .

[27]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[28]  Zhang Yi,et al.  A Novel Deep Learning-Based Collaborative Filtering Model for Recommendation System , 2019, IEEE Transactions on Cybernetics.