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Spatio-temporal algorithm for coding artifacts reduction in highly compressed video

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Warianty tytułu
PL
Przestrzenno-czasowy algorytm redukcji artefaktów w wysoce skompresowanym filmie
Języki publikacji
EN
Abstrakty
EN
Images and video are often coded using block-based discrete cosine transform (DCT) or discrete wavelet transform (DWT) which cause a great deal of visual distortions. Restoration of image sequences can obtain better results compared to restoring each image individually, provided that the temporal redundancy is adequately used. In this article, efficient approach for artifacts reduction has been presented. In order to enhance the overall video quality, the proposed approach uses image sequence redundancy. Spatial and temporal information is used for the video de-noising process.
PL
Zdjęcia i filmy są często kodowane za pomocą blokowej dyskretnej transformaty kosinusowej (DCT) lub dyskretnej transformaty falkowej (DWT), które powodują duże zakłócenia wizualne. Przywrócenie sekwencji obrazów pozwala uzyskać lepsze wyniki niż przywracanie każdego obrazu osobno, pod warunkiem odpowiedniego użycia redundancji czasowej. W niniejszym artykule zaprezentowano efektywne podejście do redukcji artefaktów. W celu zwiększenia ogólnej jakości obrazu omawiane podejście wykorzystuje redundancję sekwencji obrazu. Do procesu odszumiania filmu wykorzystano informacje czasowe i przestrzenne.
Rocznik
Strony
41--55
Opis fizyczny
Bibliogr. 29 poz., il., wz., tab.
Twórcy
autor
  • Department of Computerized Automatic Systems, Lviv Polytechnic National University
  • Department of Computerized Automatic Systems, Lviv Polytechnic National University
Bibliografia
  • [1] Fedak V.I, Nakonechny A.Y., Artifacts suppression in images and video. Non-Local Means as algorithm for reducing image and video distortions, Hluboka nad Vltavou, Czech Republic 2009.
  • [2] Fedak V., Nakonechny A., Determining motion vectors quality during the process of motion estimation, 6th International Scientific and Technical Conference 16–19 November 2011, Lviv.
  • [3] Callico, G.M. et al., Analysis of fast block matching motion estimation algorithms for video super-resolution systems, IEEE Transactions on Consumer Electronics, 54(3), 2008, 1430-1438.
  • [4] Protter M., Processing Image Sequences Without Motion Estimation, Technion-Computer Science Department, Ph.D. Thesis.
  • [5] Boulanger J., Kervrann C., Bouthemy P., Space-time adaptation for patch based image sequence restoration, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, 2007, 1096-1102.
  • [6] Dabov K., Foi A., Egiazarian K., Video denoising by sparse 3D transform-domain collaborative filtering, Proc. of the European Signal Processing Conference, EUSIPCO, 2007.
  • [7] Buades A., Image and film denoising bynon-local means, Journal of Visual Commincation and Image Representation, Vol. 1, No. 1, Ph. D. Thesis, 24 March 2004, Article No. VC970378.
  • [8] Ramamurthi B., Gersho A., Nonlinear space-variant postprocessing of block coded images, IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 34, No. 5, Oct. 1986, 1258,1268.
  • [9] Truong Quang Vinh, Young-Chul Kim, Block Artifact Reduction Based on Pixel Classification Using Binary Edge Map, 2008.
  • [10] Wang C. et al., Adaptive Reduction of Blocking Artifacts in DCT Domain for Highly Compressed Images, 2003.
  • [11] Alan W.-C. Liew, Hong Yan, IEEE Blocking Artifacts Suppression in Block-Coded Images Using Overcomplete Wavelet Representation, IEEE Trans Circuits Syst. Video Technol., Vol. 11, No. 14, No. 4, April 2004.
  • [12] Choy S.O., Chan Y.-H., Siu W.-C., Reduction of block-transform image coding artifacts by using local statistics of transform coefficients, IEEE Signal Processing Letters, Vol. 4, No. 1, Jan. 1997, 5-7.
  • [13] Truong Quang Vinh, Young-Chul Kim Block., Artifact Reduction Based on Pixel Classification Using Binary Edge Map 2008.
  • [14] Vincent O.R., Folorunso O., A Descriptive Algorithm for Sobel Image Edge Detection, Proceedings of Informing Science & IT Education Conference, InSITE, 2009.
  • [15] Tao C., Wu H.R., Bin Q., Adaptive postfiltering of transform coefficients for the reduction of blocking artifacts, IEEE Trans. Circuits Syst. Video Technol., Vol. 11, No. 5, May 2001, 594-602.
  • [16] Buades A., Coll B., Morel J.-M, A non-local algorithm for image denoising, 2006.
  • [17] De Haan, G. et al., True-motion estimation with 3-D recursive search block matching, Circuits and Systems for Video Technology, IEEE Transactions on, 3(5), 1993, 368-379, 388.
  • [18] Fedak V., Nakonechny A., Spatio-temporal Non-Local Means Algorithm for Coding Artifacts Reduction, Lviv Polytechnic National University, 2011.
  • [19] Zhou Z.-F., Shui P.-L., Contourlet-based image denoising algorithm using directional windows, Electronic Letters, 43, No. 2, 2007, 92-93.
  • [20] Olhede S.C., Hyperanalytic denoising, IEEE Transactions on Image Processing, 16 (6), 2007, 1522-1537.
  • [21] Donoho D.L., Johnstone I.M., Ideal spatial adaptation by wavelet shrinkage, Biometrika, Vol. 81, No. 3, 1994, 425-455.
  • [22] Pizurica A., Philips W., Estimating the probability of the presence of a signal of interest in multiresolution single and multiband image denoising, IEEE Transactions on Image Processing, 15, No. 3, 2006, 654-665.
  • [23] Shui P.-L., Image Denoising Algorithm via Doubly Local Wiener Filtering With Directional Windows in Wavelet Domain, IEEE Signal Processing Letters, 12, No. 6, 2005, 681-684.
  • [24] Luisier F., Blu T., Unser M., A New SURE Approach to Image Denoising: Inter Scale Orthonormal Wavelet Thresholding, IEEE Transactions on Image Processing, 16, No. 3, 2007, 593-606.
  • [25] Achim A., Kuruoglu E.E., Image Denoising Using Bivariate-Stable Distributions in the Complex Wavelet Domain, IEEE Signal Processing Letters, 12 (1), 2005, 17-20.
  • [26] Avindra F., Image Denoising with the Non-local Means Algorithm, University of Kansas, December 2010.
  • [27] Buades A., Coll B., Morel J.-M., A non-local algorithm for image denoising, Computer Vision and Pattern Recognition, 2005.
  • [28] Du Y., Kirenko I., Benchmarking of Coding Artifacts Reduction Techniques, Philips Restricted Technical Note PR-TN 2007/00681, October 2007.
  • [29] Wu H.R., Yuen M., A generalized Block-edge impairment metric for video coding, IEEE Signal Processing Letters, 4, November 1997, 317-320.
Typ dokumentu
Bibliografia
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