Distinguish True or False 4K Resolution Using Frequency Domain Analysis and Free-Energy Modelling

With the prevalence of Ultra-High Definition (UHD) display terminals, 4K resolution (38402160 pixels) contents are becoming a major selling point for online video media. However, due to the insufficiency of natural UHD contents, a large number of false 4K videos are circulating on the web. Those '4K' contents, usually being upscaled from lower resolutions, often frustrate enthusiastic consumers and are in fact a waste of stringent bandwidth resources. In this paper, we propose to distinguish natural 4K contents from false ones through frequency domain analysis. The basic assumption is that true 4K contents have much more high frequency responses than the upscaled versions. We use the free energy modelling to approximate the Human Visual System so as to minimize the impact of structural complexity of visual contents. We set up a dataset containing more than 1k original 4K frames together with upscaled versions using four widely used interpolation algorithms. Experimental results show that the proposed free-energy-based metric has an accuracy rate higher than 90%.

[1]  Wenjun Zhang,et al.  An efficient color image quality metric with local-tuned-global model , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

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

[3]  Wenjun Zhang,et al.  Using Free Energy Principle For Blind Image Quality Assessment , 2015, IEEE Transactions on Multimedia.

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

[5]  David S. Doermann,et al.  No-Reference Image Quality Assessment Using Visual Codebooks , 2012, IEEE Transactions on Image Processing.

[6]  Jianfei Cai,et al.  Cross-Dimensional Perceptual Quality Assessment for Low Bit-Rate Videos , 2008, IEEE Transactions on Multimedia.

[7]  André Kaup,et al.  Retina model inspired image quality assessment , 2013, 2013 Visual Communications and Image Processing (VCIP).

[8]  Susu Yao,et al.  GES: a new image quality assessment metric based on energy features in Gabor transform domain , 2006, 2006 IEEE International Symposium on Circuits and Systems.

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

[10]  Wenjun Zhang,et al.  Image quality assessment metrics based on multi-scale edge presentation , 2005, IEEE Workshop on Signal Processing Systems Design and Implementation, 2005..

[11]  Wei Zhang,et al.  The SJTU 4K video sequence dataset , 2013, 2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX).

[12]  Weisi Lin,et al.  A Psychovisual Quality Metric in Free-Energy Principle , 2012, IEEE Transactions on Image Processing.

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

[14]  Weisi Lin,et al.  LGPS: Phase Based Image Quality Assessment Metric , 2007, 2007 IEEE Workshop on Signal Processing Systems.

[15]  Wenjun Zhang,et al.  A new reduced-reference image quality assessment using structural degradation model , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

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