Image and video quality assessment with the use of various verification databases

• Autorzy: Okarma K.

Warianty tytułu

PL

Ocena jakości obrazu i sekwencji wideo z wykorzystaniem różnych baz testowych

• Języki publikacji: EN

Abstrakty

EN

In the paper the verification results obtained for the most of state-of-the-art image and video (using frame-by-frame approach) metrics, together with proposed combined one, using currently available databases are presented. Obtained values of the metrics have been compared to MOS and DMOS values and the linear correlation coefficients have been used for the verification of the usefulness of metrics for each type of distortions, demonstrating the advantages of the combined metric.

PL

W artykule przedstawiono wyniki weryfikacji uzyskane dla najbardziej znanych nowoczesnych wskaźników oceny jakości obrazów oraz sekwencji wideo (z wykorzystaniem analizy poklatkowej), jak również dla zaproponowanego wskaźnika złożonego, z wykorzystaniem aktualnie dostępnych baz testowych. Uzyskane wyniki wskaźników zostały porównane z ocenami subiektywnymi wyrażonymi wartościami MOS i DMOS; do weryfikacji ich zgodności użyto współczynników korelacji liniowej świadczących o przydatności poszczególnych wskaźników dla różnych rodzajów zniekształceń, jak również o zaletach wskaźnika złożonego.

Słowa kluczowe

EN

image quality assessment; combined quality metric

PL

ocena jakości obrazów; wskaźnik jakości złożony

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 3b
• Strony: 321--323
• Opis fizyczny: Bibliogr. 24 poz., tab.

Twórcy

autor

Okarma K.

• Department of Signal Processing and Multimedia Engineering, Faculty of Electrical Engineering, West Pomeranian University of Technology, 10, 26. Kwietnia Str., 71-126 Szczecin

Bibliografia

• [1] Li X., Blind Image Quality Assessment, Proc. IEEE Conf. Image Proc., Rochester, USA (2002), 449-452
• [2] Okarma K., Lech P., A Statistical Reduced-Reference Approach to Digital Image Quality Assessment, Lecture Notes in Computer Science, 5337 (2009), 43-54
• [3] Wang Z., Bovik A.C., A Universal Image Quality Index, IEEE Signal Proc. Letters, 9 (2002), n.3, 81-84
• [4] Wang Z., Bovik A.C., Sheikh H., Simoncelli P., Image Quality Assessment: From Error Measurement to Structural Similarity, IEEE Trans. Image Proc., 13 (2004), n.4, 600-612
• [5] Mansouri A., Mahmoudi-Aznaveh A., Torkamani-Azar F., Jahanshahi J.A., Image Quality Assessment Using the Singular Value Decomposition Theorem, Optical Review, 16 (2009), n. 2, 49-53
• [6] Sheikh H., Wang Z., Cormack L., Bovik A.C., LIVE Image Quality Assessment Database Release 2, Available at: http://live.ece.utexas.edu/research/quality
• [7] Ponomarenko N., Lukin V., Zelensky A., Egiazarian K., Carli M., Battisti F., TID2008 – A Database for Evaluation of Full-Reference Visual Quality Assessment Metrics, Advances of Modern Radioelectronics, 10 (2009), 30-45
• [8] Okarma K., Influence of the 2-D sliding windows on the correlation of the digital image quality assessment results using the Structural Similarity approach with the subjective evaluation, Przegląd Elektrotechniczny, 86 (2010) n.7, 109-111
• [9] Okarma K., Two-dimensional windowing in the Structural Similarity index for the colour image quality assessment, Lecture Notes in Computer Science, 5702 (2009), 501-508
• [10] Wang Z., Simoncelli E., Bovik A., Multi-Scale Structural Similarity for Image Quality Assessment. Proc. 37th IEEE Asilomar Conf. on Signals, Systems and Computers (2003)
• [11] Malpica W., Bovik A., Range Image Quality Assessment by Structural Similarity. Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Taipei (2009), 1149-1152
• [12] Li C., Bovik A., Three-component Weighted Structural Similarity Index. Proc. SPIE Image Quality and System Performance, 7242 (2009)
• [13] Shnayderman A., Gusev A., Eskicioglu A., An SVDBased Gray-Scale Image Quality Measure for Local and Global Assessment. IEEE Trans. Image Proc., 15 (2006), n.2, 422-429
• [14] Sheikh H., Bovik A., Image Information and Visual Quality. IEEE Trans. Image Proc., 15 (2006), n.2, 430-444
• [15] Video Quality Experts Group, Final Report on the Validation of Objective Models of Video Quality Assessment, (2003), Available at: http://www.vqeg.org
• [16] Okarma K., Combined Full-Reference Image Quality Metric Linearly Correlated with Subjective Assessment, Lecture Notes in Artificial Intelligence, 6113 (2010), 539-546
• [17] Okarma K., Video Quality Assessment Using the Combined Full-Reference Approach, Image Processing and Communications Challenges 2 - Advances in Intelligent and Soft Computing, 84 (2010), 51-58
• [18] Okarma K., Colour Image Quality Assessment Using the Combined Full-Reference Approach, Computer Recognition Systems 4 - Advances in Intelligent and Soft Computing, 95 (2011), 287-296
• [19] Larson E., Chandler D., Most Apparent Distortion: Full-Reference Image Quality Assessment and the Role of Strategy, Journal of Electronic Imaging, 19 (2010), n.1, 011006
• [20] Chandler D., Hemami S., VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images, IEEE Trans. Image Proc., 16 (2007), n.9, 2284-2298
• [21] Le Callet P., Autrusseau F., Subjective Quality Assessment IRCCyN/IVC Database, (2005), Available at: http://www.irccyn.ec-nantes.fr/ivcdb/
• [22] Engelke U., Kusuma T., Zepernick H.-J., Caldera M., Reduced-Reference Metric Design for Objective Perceptual Quality Assessment in Wireless Imaging, Signal Processing: Image Communication, 24 (2009), n.7, 525-547
• [23] Moorthy A., Seshadrinathan K., Soundararajan R., Bovik A., Wireless Video Quality Assessment: A Study of Subjective Scores and Objective Algorithms. IEEE Trans. Circuits and Systems for Video Technology, 20 (2010), n.4, 513-516
• [24] Seshadrinathan K., Soundararajan R., Bovik A., Cormack L., Study of Subjective and Objective Quality Assessment of Video, IEEE Trans. Image Proc., 19 (2010), n.6, 1427-1441