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Continuous wavelet transform-based no-reference quality assessment of deblocked images

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Abstract

An image when passed through a compression process either gains noise or loses some information, resulting in a degraded image. JPEG is considered to be one of the most commonly used compression standards whose resulting images are found to be subjected to blocking artifacts at low bit rates. There exist a few deblocking algorithms which have been proposed in the literature to reduce the blocking artifacts in compressed images. However, unfortunately these deblocking techniques introduce blur distortion in the images and hence the deblocked images may contain multiple distortions. Existing image quality metrics have limitations in evaluating the quality of deblocked images as they are not designed for multiply distorted images. To overcome this issue, we propose a no-reference quality assessment technique for deblocked images using continuous wavelet transform. We evaluate the proposed technique on DBID database which consists of general deblocked images. Experimental results show that the proposed quality assessment technique outperforms the existing image quality assessment techniques for deblocked images.

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References

  1. Alireza Golestaneh, S., Chandler, D.M.: Algorithm for JPEG artifact reduction via local edge regeneration. J. Electron. Imaging 23(1), 013,018 (2014). (1–14)

    Article  Google Scholar 

  2. Bahrami, K., Kot, A.C.: A fast approach for no-reference image sharpness assessment based on maximum local variation. IEEE Signal Process. Lett. 21(6), 751–755 (2014)

    Article  Google Scholar 

  3. Bovik, A., Liu, S.: DCT-domain blind measurement of blocking artifacts in DCT-coded images. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2001), vol. 3, pp. 1725–1728 (2001)

  4. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)

    Article  Google Scholar 

  5. Chen, C., Bloom, J.A.: A Blind Reference-Free Blockiness Measure, pp. 112–123. Springer, Berlin (2010)

    Google Scholar 

  6. Chen, M.J., Bovik, A.C.: No-reference image blur assessment using multiscale gradient. EURASIP J. Image Video Process. 2011(1), 1–11 (2011)

    Article  Google Scholar 

  7. Ferzli, R., Karam, L.J.: A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB). IEEE Trans. Image Process. 18(4), 717–728 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  8. Foi, A., Katkovnik, V., Egiazarian, K.: Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images. IEEE Trans. Image Process. 16(5), 1395–1411 (2007)

    Article  MathSciNet  Google Scholar 

  9. Golestaneh, S.A., Chandler, D.M.: No-reference quality assessment of JPEG images via a quality relevance map. IEEE Signal Process. Lett. 21(2), 155–158 (2014)

    Article  Google Scholar 

  10. Grossmann, A., Morlet, J.: Decomposition of hardy functions into square integrable wavelets of constant shape. SIAM J. Math. Anal. 15(4), 723–736 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  11. Hassen, R., Wang, Z., Salama, M.M.A.: Image sharpness assessment based on local phase coherence. IEEE Trans. Image Process. 22(7), 2798–2810 (2013)

    Article  Google Scholar 

  12. Lee, S., Park, S.J.: A new image quality assessment method to detect and measure strength of blocking artifacts. Signal Process. Image Commun. 27(1), 31–38 (2012)

    Article  Google Scholar 

  13. Li, L., Zhou, Y., Lin, W., Wu, J., Zhang, X., Chen, B.: No-reference quality assessment of deblocked images. Neurocomputing 177, 572–584 (2016)

    Article  Google Scholar 

  14. Li, L., Zhou, Y., Wu, J., Lin, W., Li, H.: GridSAR: grid strength and regularity for robust evaluation of blocking artifacts in JPEG images. J. Vis. Commun. Image Represent. 30, 153–163 (2015)

    Article  Google Scholar 

  15. Li, L., Zhu, H., Yang, G., Qian, J.: Referenceless measure of blocking artifacts by Tchebichef kernel analysis. IEEE Signal Process. Lett. 21(1), 122–125 (2014)

    Article  Google Scholar 

  16. Liu, H., Heynderickx, I.: A perceptually relevant no-reference blockiness metric based on local image characteristics. EURASIP J. Adv. Signal Process. 2009(1), 263540 (2009)

  17. Marziliano, P., Dufaux, F., Winkler, S., Ebrahimi, T.: Perceptual blur and ringing metrics: application to JPEG2000. Signal Process. Image Commun. 19(2), 163–172 (2004)

    Article  Google Scholar 

  18. Mittal, A., Moorthy, A., Bovik, A.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  19. Mittal, A., Soundararajan, R., Bovik, A.: Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2013)

    Article  Google Scholar 

  20. Moorthy, A., Bovik, A.: A two-step framework for constructing blind image quality indices. IEEE Signal Process. Lett. 17(5), 513–516 (2010)

    Article  Google Scholar 

  21. Moorthy, A., Bovik, A.: Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Trans. Image Process. 20(12), 3350–3364 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  22. Narvekar, N.D., Karam, L.J.: A no-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE Trans. Image Process. 20(9), 2678–2683 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  23. Ong, E., Lin, W., Lu, Z., Yang, X., Yao, S., Pan, F., Jiang, L., Moschetti, F.: A no-reference quality metric for measuring image blur. In: Proceedings of Seventh International Symposium on Signal Processing and Its Applications, vol. 1, pp. 469–472 (2003)

  24. Pan, F., Lin, X., Rahardja, S., Ong, E.P., Lin, W.S.: Using edge direction information for measuring blocking artifacts of images. Multidimens. Syst. Signal Process. 18(4), 297–308 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  25. Saad, M., Bovik, A., Charrier, C.: Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans. Image Process. 21(8), 3339–3352 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  26. Sang, Q., Qi, H., Wu, X., Li, C., Bovik, A.C.: No-reference image blur index based on singular value curve. J. Vis. Commun. Image Represent. 25(7), 1625–1630 (2014)

    Article  Google Scholar 

  27. Sun, D., Cham, W.K.: Postprocessing of low bit-rate block DCT coded images based on a fields of experts prior. IEEE Trans. Image Process. 16(11), 2743–2751 (2007)

    Article  MathSciNet  Google Scholar 

  28. Takeda, H., Farsiu, S., Milanfar, P.: Kernel regression for image processing and reconstruction. IEEE Trans. Image Process. 16(2), 349–366 (2007)

    Article  MathSciNet  Google Scholar 

  29. Vu, P.V., Chandler, D.M.: A fast wavelet-based algorithm for global and local image sharpness estimation. IEEE Signal Process. Lett. 19(7), 423–426 (2012)

    Article  Google Scholar 

  30. Wang, Z., Bovik, A., Evan, B.: Blind measurement of blocking artifacts in images. In: Proceedings of International Conference on Image Processing (ICIP 2000), vol. 3, pp. 981–984 (2000)

  31. Wu, H.R., Rao, K.R.: Digital Video Image Quality and Perceptual Coding. CRC Press, Boca Raton (2005)

    Book  Google Scholar 

  32. Ye, P., Kumar, J., Kang, L., Doermann, D.: Unsupervised feature learning framework for no-reference image quality assessment. In: Proceedings of Computer Vision and Pattern Recognition (CVPR 2012) IEEE Conference on, pp. 1098–1105 (2012)

  33. Yim, C., Bovik, A.C.: Quality assessment of deblocked images. IEEE Trans. Image Process. 20(1), 88–98 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  34. Zhai, G., Zhang, W., Yang, X., Lin, W., Xu, Y.: Efficient image deblocking based on postfiltering in shifted windows. IEEE Trans. Circuits Syst. Video Technol. 18(1), 122–126 (2008)

    Article  Google Scholar 

  35. Zhang, X., Xiong, R., Fan, X., Ma, S., Gao, W.: Compression artifact reduction by overlapped-block transform coefficient estimation with block similarity. IEEE Trans. Image Process. 22(12), 4613–4626 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  36. Zhang, Y., Chandler, D.M.: No-reference image quality assessment based on log-derivative statistics of natural scenes. J. Electron. Imaging 22(4), 043,025 (2013). (1–22)

    Article  Google Scholar 

Download references

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Correspondence to Piyush Joshi.

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Joshi, P., Prakash, S. & Rawat, S. Continuous wavelet transform-based no-reference quality assessment of deblocked images. Vis Comput 34, 1739–1748 (2018). https://doi.org/10.1007/s00371-017-1460-z

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