Papers
Topics
Authors
Recent
Search
2000 character limit reached

FS-BAND: A Frequency-Sensitive Banding Detector

Published 30 Nov 2023 in cs.CV, cs.MM, and eess.IV | (2311.18216v1)

Abstract: Banding artifact, as known as staircase-like contour, is a common quality annoyance that happens in compression, transmission, etc. scenarios, which largely affects the user's quality of experience (QoE). The banding distortion typically appears as relatively small pixel-wise variations in smooth backgrounds, which is difficult to analyze in the spatial domain but easily reflected in the frequency domain. In this paper, we thereby study the banding artifact from the frequency aspect and propose a no-reference banding detection model to capture and evaluate banding artifacts, called the Frequency-Sensitive BANding Detector (FS-BAND). The proposed detector is able to generate a pixel-wise banding map with a perception correlated quality score. Experimental results show that the proposed FS-BAND method outperforms state-of-the-art image quality assessment (IQA) approaches with higher accuracy in banding classification task.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (33)
  1. W. Zhang, K. Ma, J. Yan, D. Deng, and Z. Wang, “Blind image quality assessment using a deep bilinear convolutional neural network,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 1, pp. 36–47, 2018.
  2. V. Kazemi, A. Shahzadi, and H. K. Bizaki, “Multifocus image fusion using adaptive block compressive sensing by combining spatial frequency,” Multimedia Tools and Applications, vol. 81, no. 11, pp. 15 153–15 170, 2022.
  3. S. Li and B. Yang, “Multifocus image fusion using region segmentation and spatial frequency,” Image and vision computing, vol. 26, no. 7, pp. 971–979, 2008.
  4. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
  5. L. Bar, N. Sochen, and N. Kiryati, “Semi-blind image restoration via mumford-shah regularization,” IEEE Transactions on Image Processing, vol. 15, no. 2, pp. 483–493, 2006.
  6. K. Gu, J. Qiao, X. Min, G. Yue, W. Lin, and D. Thalmann, “Evaluating quality of screen content images via structural variation analysis,” IEEE transactions on visualization and computer graphics, vol. 24, no. 10, pp. 2689–2701, 2017.
  7. Z. Tang, Y. Zheng, K. Gu, K. Liao, W. Wang, and M. Yu, “Full-reference image quality assessment by combining features in spatial and frequency domains,” IEEE Transactions on Broadcasting, vol. 65, no. 1, pp. 138–151, 2018.
  8. G. Baugh, A. Kokaram, and F. Pitié, “Advanced video debanding,” in Proceedings of the 11th European Conference on Visual Media Production, 2014, pp. 1–10.
  9. J. W. Lee, B. R. Lim, R.-H. Park, J.-S. Kim, and W. Ahn, “Two-stage false contour detection using directional contrast and its application to adaptive false contour reduction,” IEEE Transactions on Consumer Electronics, vol. 52, no. 1, pp. 179–188, 2006.
  10. Q. Huang, H. Y. Kim, W.-J. Tsai, S. Y. Jeong, J. S. Choi, and C.-C. J. Kuo, “Understanding and removal of false contour in hevc compressed images,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 2, pp. 378–391, 2016.
  11. Z. Tu, J. Lin, Y. Wang, B. Adsumilli, and A. C. Bovik, “Bband index: a no-reference banding artifact predictor,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).   IEEE, 2020, pp. 2712–2716.
  12. P. Tandon, M. Afonso, J. Sole, and L. Krasula, “Cambi: Contrast-aware multiscale banding index,” in 2021 Picture Coding Symposium (PCS).   IEEE, 2021, pp. 1–5.
  13. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing, vol. 13, no. 4, pp. 600–612, 2004.
  14. 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, 2012.
  15. 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, 2003.
  16. A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Transactions on image processing, vol. 21, no. 12, pp. 4695–4708, 2012.
  17. A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “completely blind” image quality analyzer,” IEEE Signal processing letters, vol. 20, no. 3, pp. 209–212, 2012.
  18. Y. Wang, S.-U. Kum, C. Chen, and A. Kokaram, “A perceptual visibility metric for banding artifacts,” in 2016 IEEE International Conference on Image Processing (ICIP).   IEEE, 2016, pp. 2067–2071.
  19. A. Kapoor, J. Sapra, and Z. Wang, “Capturing banding in images: Database construction and objective assessment,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).   IEEE, 2021, pp. 2425–2429.
  20. E. A. DeYoe and D. C. Van Essen, “Concurrent processing streams in monkey visual cortex,” Trends in neurosciences, vol. 11, no. 5, pp. 219–226, 1988.
  21. C. Zhang, Z. Huang, S. Liu, and J. Xiao, “Dual-channel multi-task cnn for no-reference screen content image quality assessment,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 8, pp. 5011–5025, 2022.
  22. S. A. Al-Sumaidaee, M. A. Abdullah, R. R. O. Al-Nima, S. S. Dlay, and J. A. Chambers, “Multi-gradient features and elongated quinary pattern encoding for image-based facial expression recognition,” Pattern Recognition, vol. 71, pp. 249–263, 2017.
  23. M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13.   Springer, 2014, pp. 818–833.
  24. R. Ranjan, V. M. Patel, and R. Chellappa, “Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition,” IEEE transactions on pattern analysis and machine intelligence, vol. 41, no. 1, pp. 121–135, 2017.
  25. D. Ghadiyaram, C. Chen, S. Inguva, and A. Kokaram, “A no-reference video quality predictor for compression and scaling artifacts,” in 2017 IEEE International Conference on Image Processing (ICIP).   IEEE, 2017, pp. 3445–3449.
  26. Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” in The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, vol. 2.   Ieee, 2003, pp. 1398–1402.
  27. R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 586–595.
  28. L. Krasula, Z. Li, C. G. Bampis, M. Afonso, N. F. Miret, and J. Sole, “Banding vs. quality: perceptual impact and objective assessment,” in 2022 IEEE International Conference on Image Processing (ICIP).   IEEE, 2022, pp. 2236–2240.
  29. H. Talebi and P. Milanfar, “Nima: Neural image assessment,” IEEE transactions on image processing, vol. 27, no. 8, pp. 3998–4011, 2018.
  30. S. Su, Q. Yan, Y. Zhu, C. Zhang, X. Ge, J. Sun, and Y. Zhang, “Blindly assess image quality in the wild guided by a self-adaptive hyper network,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 3667–3676.
  31. W. Sun, X. Min, D. Tu, S. Ma, and G. Zhai, “Blind quality assessment for in-the-wild images via hierarchical feature fusion and iterative mixed database training,” IEEE Journal of Selected Topics in Signal Processing, 2023.
  32. D. Mukherjee, J. Bankoski, A. Grange, J. Han, J. Koleszar, P. Wilkins, Y. Xu, and R. Bultje, “The latest open-source video codec vp9-an overview and preliminary results,” in 2013 Picture Coding Symposium (PCS).   IEEE, 2013, pp. 390–393.
  33. J. L. Bentley, “Multidimensional binary search trees used for associative searching,” Communications of the ACM, vol. 18, no. 9, pp. 509–517, 1975.
Citations (1)
View on Semantic Scholar

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up