Full-reference video quality assessment on high-definition video content

The paper provided herein aims at investigating two state-of-the-art publicly available full-reference video quality assessment metrics, particularly with regard to high-definition video data. Concretely, we will concentrate on the performance of the Multi-Scale Structural Similarity index (MS-SSIM) and the NTIA General Video Quality Metric (VQM) compared to the still widely used Peak Signal-to-Noise Ratio (PSNR) to answer the question which metric is better suited to automatically assess the quality of a given video. Evaluation is done on progressive video material of three different video quality databases. The paper also performs a detailed examination of the evaluation methodology of objective video quality metrics itself aiming at a better reproducibility and comparability of results.

[1]  Martin Slanina,et al.  Reference free SSIM estimation for full HD video content , 2011, Proceedings of 21st International Conference Radioelektronika 2011.

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

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

[4]  D. C. Howell.  Statistical methods for psychology, 3rd ed. , 1992 .

[5]  Stephen Wolf,et al.  Application of the NTIA General Video Quality Metric (VQM) to HDTV Quality Monitoring , 2007 .

[6]  Alan C. Bovik,et al.  A subjective study to evaluate video quality assessment algorithms , 2010, Electronic Imaging.

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

[8]  河野 太一.  Video Quality Experts Group (VQEG)の最新動向(ネットワーク品質,ネットワーク計測・管理,ネットワーク仮想化,ネットワークサービス,一般) , 2013 .

[9]  Margaret H. Pinson,et al.  The Consumer Digital Video Library , 2010 .

[10]  Margaret H. Pinson,et al.  A new standardized method for objectively measuring video quality , 2004, IEEE Transactions on Broadcasting.

[11]  Martin Reisslein,et al.  Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison , 2011, IEEE Transactions on Broadcasting.

[12]  S. Tubaro,et al.  Subjective assessment of H.264/AVC video sequences transmitted over a noisy channel , 2009, 2009 International Workshop on Quality of Multimedia Experience.

[13]  Christian Keimel,et al.  Improving the verification process of video quality metrics , 2009, 2009 International Workshop on Quality of Multimedia Experience.

[14]  Mislav Grgic,et al.  Comparison of HDTV formats using objective video quality measures , 2009, Multimedia Tools and Applications.

[15]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[16]  Stefano Tubaro,et al.  A H.264/AVC video database for the evaluation of quality metrics , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

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

[18]  Jari Korhonen.  Audiovisual quality assessment in communications applications: Current status, trends and challenges , 2010, 2010 International Symposium on Intelligent Signal Processing and Communication Systems.

[19]  D. C. Howell.  Statistical Methods for Psychology , 1987 .

[20]  Maria Kihl,et al.  Evaluation of video quality metrics on transmission distortions in H.264 coded video , 2011, 2011 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB).

[21]  Sugato Chakravarty,et al.  Methodology for the subjective assessment of the quality of television pictures , 1995 .

[22]  Tengyu Ma,et al.  CS229 Lecture notes , 2007 .

[23]  Zhou Wang,et al.  Video quality assessment based on structural distortion measurement , 2004, Signal Process. Image Commun..