A Study on Quality Assessment for Medical Ultrasound Video Compressed via HEVC

The quality of experience and quality of service provided in the healthcare sector are critical in evaluating the reliable delivery of the healthcare services provided. Medical images and videos play a major role in modern e-health services and have become an integral part of medical data communication systems. The quality evaluation of medical images and videos is an essential process, and one of the ways of addressing it is via the use of quality metrics. In this paper, we evaluate the performance of seven state-of-the-art video quality metrics with respect to compressed medical ultrasound video sequences. We study the performance of each video quality metric in representing the diagnostic quality of the video, by evaluating the correlation of each metric with the subjective opinions of medical experts. The results indicate that the visual information fidelity, structural similarity index, and universal quality index metrics show good correlation with the subjective scores provided by medical experts. The tests also investigate the performance of the emerging video compression standard, high-efficiency video coding-HEVC, for medical ultrasound video compression. The results show that, using HEVC with the considered ultrasound video sequences, a diagnostically reliable compressed ultrasound video can be obtained for compression with values of the quantization parameter up to 35.

[1]  Kjell Brunnström,et al.  VQeg validation and ITU standardization of objective perceptual video quality metrics [Standards in a Nutshell] , 2009, IEEE Signal Processing Magazine.

[2]  Samir Chatterjee,et al.  Internet-based telemedicine: An empirical investigation of objective and subjective video quality , 2008, Decis. Support Syst..

[3]  Maria G. Martini,et al.  Medical image and video quality assessment in e-health applications and services , 2013, 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013).

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

[5]  Nada Y. Philip,et al.  Medical QoS provision based on reinforcement learning in ultrasound streaming over 3.5G wireless systems , 2009, IEEE Journal on Selected Areas in Communications.

[6]  Christophe Rosenberger,et al.  Towards a New Tool for the Evaluation of the Quality of Ultrasound Compressed Images , 2006, IEEE Transactions on Medical Imaging.

[7]  Jean-Marie Moureaux,et al.  Subjective MPEG2 compressed video quality assessment: Application to Tele-surgery , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[8]  Álvaro Alesanco Iglesias,et al.  SPIHT-Based Echocardiogram Compression: Clinical Evaluation and Recommendations of Use , 2013, IEEE Journal of Biomedical and Health Informatics.

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

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

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

[12]  Maria G. Martini Wireless broadband multimedia health services: Current status and emerging concepts , 2008, 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications.

[13]  Hiroshi Tanaka,et al.  Qualitative and quantitative assessment of video transmitted by DVTS (digital video transport system) in surgical telemedicine , 2007, Journal of telemedicine and telecare.

[14]  M.G. Martini,et al.  Quality Driven Wireless Video Transmission for Medical Applications , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  ITU-T Rec. P.910 (04/2008) Subjective video quality assessment methods for multimedia applications , 2009 .

[16]  Matteo Mazzotti,et al.  Robust Multilayer Control for Enhanced Wireless Telemedical Video Streaming , 2010, IEEE Transactions on Mobile Computing.

[17]  Margaret H. Pinson,et al.  Comparing subjective video quality testing methodologies , 2003, Visual Communications and Image Processing.

[18]  J García,et al.  A clinical distortion index for compressed echocardiogram evaluation: recommendations for Xvid codec. , 2009, Physiological measurement.

[19]  Zhiping Lin,et al.  Applications and improvement of H.264 in medical video compression , 2005, IEEE Transactions on Circuits and Systems I: Regular Papers.

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

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

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

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

[24]  Ljiljana Platisa,et al.  Subjective and objective quality evaluation of compressed medical video sequences , 2011, QoMEX 2011.

[25]  Yu Han,et al.  A new image fusion performance metric based on visual information fidelity , 2013, Inf. Fusion.

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

[27]  Gary J. Sullivan,et al.  High Efficiency Video Coding (HEVC), Algorithms and Architectures , 2014, Integrated Circuits and Systems.