Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Cut-FUNQUE: An Objective Quality Model for Compressed Tone-Mapped High Dynamic Range Videos (2404.13452v1)

Published 20 Apr 2024 in eess.IV and cs.CV

Abstract: High Dynamic Range (HDR) videos have enjoyed a surge in popularity in recent years due to their ability to represent a wider range of contrast and color than Standard Dynamic Range (SDR) videos. Although HDR video capture has seen increasing popularity because of recent flagship mobile phones such as Apple iPhones, Google Pixels, and Samsung Galaxy phones, a broad swath of consumers still utilize legacy SDR displays that are unable to display HDR videos. As result, HDR videos must be processed, i.e., tone-mapped, before streaming to a large section of SDR-capable video consumers. However, server-side tone-mapping involves automating decisions regarding the choices of tone-mapping operators (TMOs) and their parameters to yield high-fidelity outputs. Moreover, these choices must be balanced against the effects of lossy compression, which is ubiquitous in streaming scenarios. In this work, we develop a novel, efficient model of objective video quality named Cut-FUNQUE that is able to accurately predict the visual quality of tone-mapped and compressed HDR videos. Finally, we evaluate Cut-FUNQUE on a large-scale crowdsourced database of such videos and show that it achieves state-of-the-art accuracy.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (60)
  1. IEC, “Multimedia systems and equipment - colour measurement and management - part 2-1: Colour management - default RGB colour space - sRGB,” 1999.
  2. ITU-R, “ITU-R BT.709: Parameter values for the hdtv standards for production and international programme exchange,” 2011.
  3. ——, “ITU-R BT.2100: Image parameter values for high dynamic range television for use in production and international programme exchange,” 2018.
  4. S. Standard, “High dynamic range electro-optical transfer function of mastering reference displays,” SMPTE ST, vol. 2084, no. 2014, p. 11, 2014.
  5. T. Borer and A. Cotton, “A display-independent high dynamic range television system,” SMPTE Motion Imaging Journal, vol. 125, no. 4, pp. 50–56, 2016.
  6. CTA. Television technology consumer definitions. [Online]. Available: https://cdn.cta.tech/cta/media/media/membership/pdfs/videotechnology-consumer-definitions.pdf
  7. HDR10+ Technologies, LLC. (2019) HDR10+ system whitepaper. [Online]. Available: https://hdr10plus.org/wp-content/uploads/2019/08/HDR10_WhitePaper.pdf
  8. C. Forrester, “SkyPerfect offers UHD-HDR by DTH.” [Online]. Available: https://advanced-television.com/2015/11/04/skyperfect-offers-uhd-hdr-by-dth/
  9. (2016) An introduction to Dolby Vision. [Online]. Available: https://professional.dolby.com/siteassets/pdfs/dolby-vision-whitepaper_an-introduction-to-dolby-vision_0916.pdf
  10. M. Čadík, M. Wimmer, L. Neumann, and A. Artusi, “Evaluation of HDR tone mapping methods using essential perceptual attributes,” Computers & Graphics, vol. 32, no. 3, pp. 330–349, 2008.
  11. J. Morovic and M. R. Luo, “The fundamentals of gamut mapping: A survey,” Journal of Imaging Science and Technology, vol. 45, no. 3, pp. 283–290, 2001.
  12. A. K. Venkataramanan, C. Stejerean, and A. C. Bovik, “FUNQUE: Fusion of unified quality evaluators,” in 2022 IEEE International Conference on Image Processing (ICIP), 2022, pp. 2147–2151.
  13. A. K. Venkataramanan, C. Stejerean, I. Katsavounidis, and A. C. Bovik, “One transform to compute them all: Efficient fusion-based full-reference video quality assessment,” IEEE Transactions on Image Processing, vol. 33, pp. 509–524, 2024.
  14. T. O. Aydin, R. Mantiuk, K. Myszkowski, and H.-P. Seidel, “Dynamic range independent image quality assessment,” ACM Trans. Graph., vol. 27, no. 3, p. 1–10, aug 2008. [Online]. Available: https://doi.org/10.1145/1360612.1360668
  15. H. Yeganeh and Z. Wang, “Objective quality assessment of tone-mapped images,” IEEE Transactions on Image Processing, vol. 22, no. 2, pp. 657–667, 2013.
  16. Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, 2004.
  17. D. Kundu and B. L. Evans, “Visual attention guided quality assessment of tone-mapped images using scene statistics,” in 2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 96–100.
  18. 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.
  19. 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, 2013.
  20. H. Ziaei Nafchi, A. Shahkolaei, R. Farrahi Moghaddam, and M. Cheriet, “FSITM: A feature similarity index for tone-mapped images,” IEEE Signal Processing Letters, vol. 22, no. 8, pp. 1026–1029, 2015.
  21. L. Zhang, L. Zhang, X. 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, 2011.
  22. L. Krasula, K. Fliegel, and P. Le Callet, “FFTMI: Features fusion for natural tone-mapped images quality evaluation,” IEEE Transactions on Multimedia, vol. 22, no. 8, pp. 2038–2047, 2020.
  23. L. Krasula, M. Narwaria, K. Fliegel, and P. L. Callet, “Rendering of HDR content on LDR displays: an objective approach,” in Applications of Digital Image Processing XXXVIII, A. G. Tescher, Ed., vol. 9599, International Society for Optics and Photonics.   SPIE, 2015, p. 95990X. [Online]. Available: https://doi.org/10.1117/12.2186388
  24. H. Yeganeh, S. Wang, K. Zeng, M. Eisapour, and Z. Wang, “Objective quality assessment of tone-mapped videos,” in 2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 899–903.
  25. K. Gu, S. Wang, G. Zhai, S. Ma, X. Yang, W. Lin, W. Zhang, and W. Gao, “Blind quality assessment of tone-mapped images via analysis of information, naturalness, and structure,” IEEE Transactions on Multimedia, vol. 18, no. 3, pp. 432–443, 2016.
  26. D. Kundu, D. Ghadiyaram, A. C. Bovik, and B. Evans, “No-reference quality assessment of tone-mapped HDR pictures,” IEEE Transactions on Image Processing, vol. 26, no. 6, pp. 2957–2971, 2017.
  27. C. S. Ravuri, R. Sureddi, S. V. R. Dendi, S. Raman, and S. S. Channappayya, “Deep no-reference tone mapped image quality assessment,” in 2019 53rd Asilomar Conference on Signals, Systems, and Computers.   IEEE, 2019, pp. 1906–1910.
  28. Q. He, D. Li, T. Jiang, and M. Jiang, “Quality assessment for tone-mapped HDR images using multi-scale and multi-layer information,” in 2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2018, pp. 1–6.
  29. M. A. Saad, A. C. Bovik, and C. Charrier, “Blind prediction of natural video quality,” IEEE Transactions on Image Processing, vol. 23, no. 3, pp. 1352–1365, 2014.
  30. Z. Tu, Y. Wang, N. Birkbeck, B. Adsumilli, and A. C. Bovik, “UGC-VQA: Benchmarking blind video quality assessment for user generated content,” IEEE Transactions on Image Processing, vol. 30, pp. 4449–4464, 2021.
  31. Z. Li, A. Aaron, I. Katsavounidis, A. Moorthy, and M. Manohara, “Toward a practical perceptual video quality metric,” The Netflix Tech Blog, vol. 6, p. 2, 2016.
  32. A. K. Venkataramanan, C. Stejerean, I. Katsavounidis, and A. C. Bovik, “A funque approach to the quality assessment of compressed hdr videos,” arXiv preprint arXiv:2312.08524, 2023.
  33. R. K. Mantiuk and M. Azimi, “PU21: A novel perceptually uniform encoding for adapting existing quality metrics for HDR,” in 2021 Picture Coding Symposium (PCS), 2021, pp. 1–5.
  34. J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “Making video quality assessment models robust to bit depth,” IEEE Signal Processing Letters, vol. 30, pp. 488–492, 2023.
  35. S. Li, F. Zhang, L. Ma, and K. N. Ngan, “Image quality assessment by separately evaluating detail losses and additive impairments,” IEEE Transactions on Multimedia, vol. 13, no. 5, pp. 935–949, 2011.
  36. R. Soundararajan and A. C. Bovik, “Video quality assessment by reduced reference spatio-temporal entropic differencing,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 4, pp. 684–694, 2013.
  37. R. K. Mantiuk, M. Kim, M. Ashraf, Q. Xu, M. R. Luo, J. Martinovic, and S. Wuerger, “Practical color contrast sensitivity functions for luminance levels up to 10000 cd/m2,” Color and Imaging Conference, vol. 2020, no. 28, pp. 1–6, 2020. [Online]. Available: https://www.ingentaconnect.com/content/ist/cic/2020/00002020/00000028/art00002
  38. A. M. Derrington, J. Krauskopf, and P. Lennie, “Chromatic mechanisms in lateral geniculate nucleus of macaque.” The Journal of physiology, vol. 357, no. 1, pp. 241–265, 1984.
  39. K. Gu, M. Liu, G. Zhai, X. Yang, and W. Zhang, “Quality assessment considering viewing distance and image resolution,” IEEE Transactions on Broadcasting, vol. 61, no. 3, pp. 520–531, 2015.
  40. A. B. Watson, G. Y. Yang, J. A. Solomon, and J. Villasenor, “Visibility of wavelet quantization noise,” IEEE Transactions on Image Processing, vol. 6, no. 8, pp. 1164–1175, 1997.
  41. A. K. Venkataramanan, C. Wu, A. C. Bovik, I. Katsavounidis, and Z. Shahid, “A hitchhiker’s guide to structural similarity,” IEEE Access, vol. 9, pp. 28 872–28 896, 2021.
  42. H. R. Sheikh and A. C. Bovik, “Image information and visual quality,” IEEE Transactions on image processing, vol. 15, no. 2, pp. 430–444, 2006.
  43. K. Sharifi and A. Leon-Garcia, “Estimation of shape parameter for generalized gaussian distributions in subband decompositions of video,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 5, no. 1, pp. 52–56, 1995.
  44. N.-E. Lasmar, Y. Stitou, and Y. Berthoumieu, “Multiscale skewed heavy tailed model for texture analysis,” in 2009 16th IEEE International Conference on Image Processing (ICIP), 2009, pp. 2281–2284.
  45. Z. Wang, E. Simoncelli, and A. Bovik, “Multiscale structural similarity for image quality assessment,” in The Thirty-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, vol. 2, 2003, pp. 1398–1402 Vol.2.
  46. A. K. Venkataramanan and A. C. Bovik, “Subjective quality assessment of compressed tone-mapped high dynamic range videos,” Manuscript Under Preparation, vol. 1, 2024.
  47. VideoLAN, “x264.” [Online]. Available: https://code.videolan.org/videolan/x264.git
  48. J. Hable. Uncharted 2: HDR lighting. [Online]. Available: https://www.gdcvault.com/play/1012351/Uncharted-2-HDR
  49. E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, “Photographic tone reproduction for digital images,” in Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, ser. SIGGRAPH ’02.   Association for Computing Machinery, 2002, p. 267–276.
  50. F. Durand and J. Dorsey, “Fast bilateral filtering for the display of high-dynamic-range images,” in Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, ser. SIGGRAPH ’02.   New York, NY, USA: Association for Computing Machinery, 2002, p. 257–266.
  51. Q. Shan, T. DeRose, and J. Anderson, “Tone mapping high dynamic range videos using wavelets,” Pixar Technical Memo, 2012.
  52. E. Reinhard, T. Pouli, T. Kunkel, B. Long, A. Ballestad, and G. Damberg, “Calibrated Image Appearance Reproduction,” ACM Trans. Graph., vol. 31, no. 6, Nov 2012.
  53. G. Eilertsen, R. K. Mantiuk, and J. Unger, “Real-time noise-aware tone mapping,” ACM Trans. Graph., vol. 34, no. 6, Nov 2015.
  54. M. Oskarsson, “Temporally consistent tone mapping of images and video using optimal k-means clustering,” Journal of Mathematical Imaging and Vision, vol. 57, no. 2, pp. 225–238, Feb 2017.
  55. A. Rana, P. Singh, G. Valenzise, F. Dufaux, N. Komodakis, and A. Smolic, “Deep tone mapping operator for high dynamic range images,” IEEE Transactions on Image Processing, vol. 29, pp. 1285–1298, 2020.
  56. J. Yang, Z. Liu, M. Lin, S. Yanushkevich, and O. Yadid-Pecht, “Deep reformulated laplacian tone mapping,” arXiv preprint arXiv:2102.00348, 2021.
  57. ITU-R, “ITU-R BT.2446: Methods for conversion of high dynamic range content to standard dynamic range content and vice-versa,” 2021.
  58. A. K. Moorthy and A. C. Bovik, “Blind image quality assessment: From natural scene statistics to perceptual quality,” IEEE Transactions on Image Processing, vol. 20, no. 12, pp. 3350–3364, 2011.
  59. S. Browne, J. Dongarra, N. Garner, G. Ho, and P. Mucci, “A portable programming interface for performance evaluation on modern processors,” The International Journal of High Performance Computing Applications, vol. 14, no. 3, pp. 189–204, 2000. [Online]. Available: https://doi.org/10.1177/109434200001400303
  60. M. Delacre, D. Lakens, and C. Leys, “Why psychologists should by default use Welch’s t-test instead of Student’s t-test,” International Review of Social Psychology, vol. 30, no. 1, pp. 92–101, 2017.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now