Ambiguity-based evaluation of objective quality metrics for image compression

While results of subjective quality assessment are represented by mean opinion scores and corresponding confidence intervals, the output of an objective quality metric for a given stimulus is only a single estimated quality level. Accordingly, the performance of a metric is evaluated by measuring the accuracy of its outputs with respect to the corresponding subjective scores. However, the concept of the ambiguity interval for objective quality has been raised recently. In this paper, we propose to consider not only the accuracy but also the ambiguity of objective quality metrics for performance evaluation. In particular, we conduct benchmarking of the seven state-of-the-art image quality metrics for images compressed with JPEG and JPEG2000. It is demonstrated that the best metric in terms of accuracy may not be the best in terms of ambiguity.

[1]  Ajay Luthra,et al.  Overview of the H.264/AVC video coding standard , 2003, IEEE Trans. Circuits Syst. Video Technol..

[2]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1991, CACM.

[3]  Stefan Winkler,et al.  Mean opinion score (MOS) revisited: methods and applications, limitations and alternatives , 2016, Multimedia Systems.

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

[5]  Touradj Ebrahimi,et al.  The JPEG 2000 still image compression standard , 2001, IEEE Signal Process. Mag..

[6]  Tobias Hoßfeld,et al.  QoE beyond the MOS: Added value using quantiles and distributions , 2015, 2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX).

[7]  Francesca De Simone,et al.  Performance analysis of VP8 image and video compression based on subjective evaluations , 2011, Optical Engineering + Applications.

[8]  Manoranjan Paul,et al.  Just Noticeable Difference for Images With Decomposition Model for Separating Edge and Textured Regions , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Zhou Wang,et al.  Multi-scale structural similarity for image quality assessment , 2003 .

[10]  Alan C. Bovik,et al.  Image information and visual quality , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  Gary J. Sullivan,et al.  Overview of the High Efficiency Video Coding (HEVC) Standard , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Jong-Seok Lee,et al.  Evaluation of objective quality metrics for multidimensional video scalability , 2016, J. Vis. Commun. Image Represent..

[13]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

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

[15]  Zhou Wang,et al.  Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[16]  Rajiv Soundararajan,et al.  Study of Subjective and Objective Quality Assessment of Video , 2010, IEEE Transactions on Image Processing.

[17]  Wilson S. Geisler,et al.  Image quality assessment based on a degradation model , 2000, IEEE Trans. Image Process..

[18]  Jong-Seok Lee Comparison of objective quality metrics on the scalable extension of H.264/AVC , 2012, 2012 19th IEEE International Conference on Image Processing.

[19]  Sheila S. Hemami,et al.  VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images , 2007, IEEE Transactions on Image Processing.

[20]  Manri Cheon,et al.  On ambiguity of objective image quality assessment , 2016 .