Assessment of the spatio-temporal vector median filtering algorithms using colour video quality metrics

• Autorzy: Okarma K.
• Konferencja: Computer Applications in Electrical Engineering 2011 (11-13.04.2011; Poznań, Polska)
• Języki publikacji: EN

Abstrakty

EN

In this paper the application of the combined video quality assessment method as well as some other recently developed objective metrics for the analysis of the results of the nonlinear colour video filtering is discussed. The spatio-temporal versions of colour image filtering methods, including the Vector Median Filter, can be obtained using frame-by-frame approach but the proper choice of the spatio-temporal kernel weights and the colour space used during filtration should be based on a reliable video quality assessment. In some earlier papers the combined video quality assessment method has been proposed, which has a highly linear correlation with subjective quality scores and can be extended into the colour version. As the illustration of the problem, some results of the colour video denoising using the spatio-temporal VMF, also in a weighted version, together with the quality assessment results have been presented in the paper.

Słowa kluczowe

EN

Vector Median Filter; VMF; video quality; image quality

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Computer Applications in Electrical Engineering
• Rocznik: 2011
• Tom: Vol. 9
• Strony: 200--212
• Opis fizyczny: Bibliogr. 14 poz., rys.

Twórcy

autor

Okarma K.

• West Pomerania University of Technology 71-126 Szczecin, ul. 26. Kwietnia 10

Bibliografia

• [1] Astola J., Haavisto P., Neuvo Y.: Vector Median Filters. Proceedings of IEEE, vol. 78 no. 4 (1990), pp. 678-689.
• [2] Bartkowiak M., Domański M.: Vector median filters for processing of color images in various colour spaces. Proceedings of the 5-th International Conference on Image Processing and its Applications, Edinburgh, UK, 1995, pp. 833-836.
• [3] Louverdis G., Vardavoulia M., Andreadis I., Tsalides Ph.: A new approach to morphological color image processing. Pattern Recognition, vol. 35 (2002), pp. 1733-1741.
• [4] Mansouri A., Mahmoudi-Aznaveh A., Torkamani-Azar F., Jahanshahi J.: Image Quality Assessment Using the Singular Value Decomposition Theorem. Optical Review, vol. 16 no. 2 (2009), pp. 49-53.
• [5] Okarma K.: Combined Full-Reference Image Quality Metric Linearly Correlated with Subjective Assessment. Lecture Notes in Artificial Intelligence, vol. 6113 (2010), pp. 539-546.
• [6] Okarma K.: Video Quality Assessment Using the Combined Full-Reference Approach. Advances in Intelligent and Soft Computing, 2010, vol. 84 (Image Processing and Communications Challenges 2), pp. 51-58.
• [7] Okarma K.: Colour Image Quality Assessment Using the Combined Full-Reference Approach. Advances in Intelligent and Soft Computing, 2011, vol. 95 (Computer Recognition Systems 4), pp. 287-296.
• [8] 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, vol. 10 (2009), pp. 30-45.
• [9] Sheikh H., Bovik A.: Image Information and Visual Quality. IEEE Transactions on Image Processing, vol. 15 no. 2 (2006), pp. 430-444.
• [10] Vardavoulia M., Andreadis A., Tsalides Ph.: A New Vector Median Filter for Colour Image Processing. Pattern Recognition Letters, vol. 22 (2001), pp. 675-689.
• [11] Wang Z., Bovik A., Sheikh H., Simoncelli E.: Image Quality Assessment: From Error Measurement to Structural Similarity. IEEE Transactions on Image Processing, vol. 13 no. 4 (2004), pp. 600-612.
• [12] Wang Z., Simoncelli E., Bovik A.: Multi-Scale Structural Similarity for Image Quality Assessment. Proc. 37th IEEE Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, 2003, pp. 1398-1402.
• [13] Zhang L., Zhang L., Mou X.: RFSIM: A Feature Based Image Quality Assessment Metric Using Riesz Transforms. Proceedings of the 17th IEEE International Conference on Image Processing (ICIP), Hong Kong, China, 2010, pp. 321-324.
• [14] Zhang L., Zhang L., Mou X., Zhang D.: FSIM: A Feature Similarity Index for Image Quality Assessment. IEEE Transactions on Image Processing, vol. 20 no 8 (2011), pp. 2378-2386.