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Post-Processing for JPEG-Coded Image Deblocking via Sparse Representation and Adaptive Residual Threshold

  • Wang, Liping (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Zhou, Xiao (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Wang, Chengyou (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Jiang, Baochen (School of Mechanical, Electrical and Information Engineering, Shandong University)
  • Received : 2016.06.06
  • Accepted : 2017.01.11
  • Published : 2017.03.31

Abstract

The problem of blocking artifacts is very common in block-based image and video compression, especially at very low bit rates. In this paper, we propose a post-processing method for JPEG-coded image deblocking via sparse representation and adaptive residual threshold. This method includes three steps. First, we obtain the dictionary by online dictionary learning and the compressed images. The dictionary is then modified by the histogram of oriented gradient (HOG) feature descriptor and K-means cluster. Second, an adaptive residual threshold for orthogonal matching pursuit (OMP) is proposed and used for sparse coding by combining blind image blocking assessment. At last, to take advantage of human visual system (HVS), the edge regions of the obtained deblocked image can be further modified by the edge regions of the compressed image. The experimental results show that our proposed method can keep the image more texture and edge information while reducing the image blocking artifacts.

Keywords

References

  1. G. K. Wallace, "The JPEG still picture compression standard," Communications of the ACM, vol. 34, no. 4, pp. 30-44, Apr. 1991.
  2. T. Wiegand, G. J. Sullivan, G. Bjontegaard, and A. Luthra, "Overview of the H.264/AVC video coding standard," IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no. 7, pp. 560-576, Jul. 2003. https://doi.org/10.1109/TCSVT.2003.815165
  3. G. J. Sullivan, J.-R. Ohm, W.-J. Han, and T. Wiegand, "Overview of the high efficiency video coding (HEVC) standard," IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 12, pp. 1649-1668, Dec. 2012. https://doi.org/10.1109/TCSVT.2012.2221191
  4. M.-Y. Shen and C.-C. J. Kuo, "Review of postprocessing techniques for compression artifact removal," Journal of Visual Communication and Image Representation, vol. 9, no. 1, pp. 2-14, Mar. 1998. https://doi.org/10.1006/jvci.1997.0378
  5. C.-H. Yeh, L.-W. Kang, Y.-W. Chiou, C.-W. Lin, and S.-J. F. Jiang, "Self-learning-based post-processing for image/video deblocking via sparse representation," Journal of Visual Communication and Image Representation, vol. 25, no. 5, pp. 891-903, Jul. 2014. https://doi.org/10.1016/j.jvcir.2014.02.012
  6. G. T. Zhai, W. J. Zhang, X. K. Yang, W. S. Lin, and Y. Xu, "Efficient image deblocking based on postfiltering in shifted windows," IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 1, pp. 122-126, Jan. 2008. https://doi.org/10.1109/TCSVT.2007.906942
  7. C. Wang, J. Zhou, and S. Liu, "Adaptive non-local means filter for image deblocking," Signal Processing: Image Communication, vol. 28, no. 5, pp. 522-530, May 2013. https://doi.org/10.1016/j.image.2013.01.006
  8. G. T. Zhai, W. J. Zhang, X. K. Yang, W. S. Lin, and Y. Xu, "Efficient deblocking with coefficient regularization, shape-adaptive filtering, and quantization constraint," IEEE Transactions on Multimedia, vol. 10, no. 5, pp. 735-745, Aug. 2008. https://doi.org/10.1109/TMM.2008.922849
  9. J. Kim, "Adaptive blocking artifact reduction using wavelet-based block analysis," IEEE Transactions on Consumer Electronics, vol. 55, no. 2, pp. 933-940, May 2009. https://doi.org/10.1109/TCE.2009.5174477
  10. J. Zhang, S. W. Ma, Y. B. Zhang, and W. Gao, "Image deblocking using group-based sparse representation and quantization constraint prior," in Proc. of the IEEE International Conference on Image Processing, Quebec City, QC, Canada, Sept. 27-30, pp. 306-310, 2015.
  11. Y. Kim, C.-S. Park, and S.-J. Ko, "Fast POCS based post-processing technique for HDTV," IEEE Transaction on Consumer Electronics, vol. 49, no. 4, pp. 1438-1447, Nov. 2003. https://doi.org/10.1109/TCE.2003.1261252
  12. H. Noda and M. Niimi, "Local MAP estimation for quality improvement of compressed color images," Pattern Recognition, vol. 44, no. 4, pp. 788-793, Apr. 2011. https://doi.org/10.1016/j.patcog.2010.10.022
  13. K. Bredies and M. Holler, "A total variation-based JPEG decompression model," SIAM Journal on Imaging Sciences, vol. 5, no. 1, pp. 366-393, Jan. 2012. https://doi.org/10.1137/110833531
  14. K. Bredies and M. Holler, "Artifact-free decompression and zooming of JPEG compressed images with total generalized variation," in Proc. of the 7th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Rome, Italy, Feb. 24-26, pp. 242-258, 2012.
  15. G. M. Farinella and S. Battiato, "On the application of structured sparse model selection to JPEG compressed images," in Proc. of the 3rd International Workshop on Computational Color Imaging, Milan, Italy, Apr. 20-21, 2011, pp. 137-151.
  16. M. Elad and M. Aharon, "Image denoising via sparse and redundant representations over learned dictionaries," IEEE Transactions on Image Processing, vol. 15, no. 12, pp. 3736-3745, Dec. 2006. https://doi.org/10.1109/TIP.2006.881969
  17. Y.-M. Huang, L. Moisan, M. K. Ng, and T. Y. Zeng, "Multiplicative noise removal via a learned dictionary," IEEE Transactions on Image Processing, vol. 21, no. 11, pp. 4534-4543, Nov. 2012. https://doi.org/10.1109/TIP.2012.2205007
  18. L. Y. Ma, L. Moisan, J. Yu, and T. Y. Zeng, "A dictionary learning approach for Poisson image deblurring," IEEE Transactions on Medical Imaging, vol. 32, no. 7, pp. 1277-1289, Jul. 2013. https://doi.org/10.1109/TMI.2013.2255883
  19. J. C. Yang, J. Wright, T. Huang, and Y. Ma, "Image super-resolution as sparse representation of raw image patches," in Proc. of the 26th IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, Jun. 23-28, 8 pages, 2008.
  20. C. Jung, L. C. Jiao, H. T. Qi, and T. Sun, "Image deblocking via sparse representation," Signal Processing: Image Communication, vol. 27, no. 6, pp. 663-677, Jul. 2012. https://doi.org/10.1016/j.image.2012.03.002
  21. M. Aharon, M. Elad, and A. Bruckstein, "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation," IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311-4322, Nov. 2006. https://doi.org/10.1109/TSP.2006.881199
  22. J. A. Tropp and A. C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Transactions on Information Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007. https://doi.org/10.1109/TIT.2007.909108
  23. H. B. Chang, M. K. Ng, and T. Y. Zeng, "Reducing artifacts in JPEG decompression via a learned dictionary," IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 718-728, Feb. 2014. https://doi.org/10.1109/TSP.2013.2290508
  24. J. Mairal, F. Bach, J. Ponce, and G. Sapiro, "Online learning for matrix factorization and sparse coding," Journal of Machine Learning Research, vol. 11, no. 1, pp. 19-60, Jan. 2010.
  25. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, Jun. 20-25, vol. 1, pp. 886-893, 2005.
  26. Y. N. Zhang, B. T. Pham, and M. P. Eckstein, "The effect of nonlinear human visual system components on performance of a channelized Hotelling observer in structured backgrounds," IEEE Transactions on Medical Imaging, vol. 25, no. 10, pp. 1348-1362, Oct. 2006. https://doi.org/10.1109/TMI.2006.880681
  27. S. S. Chen, D. L. Donoho, and M. A. Saunders, "Atomic decomposition by basis pursuit," SIAM Review, vol. 43, no. 1, pp. 129-159, Mar. 2001. https://doi.org/10.1137/S003614450037906X
  28. S. G. Mallat and Z. F. Zhang, "Matching pursuits with time-frequency dictionaries," IEEE Transactions on Signal Processing, vol. 41, no. 12, pp. 3397-3415, Dec. 1993. https://doi.org/10.1109/78.258082
  29. K. Engan, S. O. Aase, and J. H. Husoy, "Frame based signal compression using method of optimal directions (MOD)," in Proc. of the IEEE International Symposium on Circuits and Systems, Orlando, FL, USA, May 30 - Jun. 2, vol. 4, 4 pages, 1999.
  30. M. S. Lewicki and T. J. Sejnowski, "Learning overcomplete representations," Neural Computation, vol. 12, no. 2, pp. 337-365, Feb. 2000. https://doi.org/10.1162/089976600300015826
  31. R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng, "Self-taught learning: Transfer learning from unlabeled data," in Proc. of the 24th International Conference on Machine Learning, Corvalis, OR, USA, Jun. 20-24, pp. 759-766, 2007.
  32. S. Z. Liu and A. C. Bovik, "Efficient DCT-domain blind measurement and reduction of blocking artifacts," IEEE Transactions on Circuits and Systems for Video Technology, vol. 12, no. 12, pp. 1139-1149, Dec. 2002. https://doi.org/10.1109/TCSVT.2002.806819
  33. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Transaction on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004. https://doi.org/10.1109/TIP.2003.819861
  34. N. Ponomarenko, F. Silvestri, K. Egiazarian, M. Carli, J. Astola, and V. Lukin, "On between-coefficient contrast masking of DCT basis functions," in Proc. of the 3rd International Workshop on Video Processing and Quality Metrics for Consumer Electronics, Scottsdale, AZ, USA, Jan. 25-26, 4 pages, 2007.
  35. L. Zhang, L. Zhang, X. Q. Mou, and D. Zhang, "FSIM: A feature similarity index for image quality assessment," IEEE Transactions on Image Processing, vol. 20, no. 8, pp. 2378-2386, Aug. 2011. https://doi.org/10.1109/TIP.2011.2109730