Quality Assessment for High Dynamic Range Stereoscopic Omnidirectional Image System | SpringerLink
Skip to main content

Quality Assessment for High Dynamic Range Stereoscopic Omnidirectional Image System

  • Conference paper
  • First Online:
Advanced Concepts for Intelligent Vision Systems (ACIVS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14124))

  • 321 Accesses

Abstract

This paper focuses on visual experience of high dynamic range (HDR) stereoscopic omnidirectional image (HSOI) system, which includes such as HSOI generation, encoding/decoding, tone mapping (TM) and terminal visualization. From the perspective of quantifying coding distortion and TM distortion in HSOI system, a “no-reference (NR) plus reduced-reference (RR)” HSOI quality assessment method is proposed by combining Retinex theory and two-layer distortion simulation of HSOI system. The NR module quantizes coding distortion for HDR images only with coding distortion. The RR module mainly measures the effect of TM operator based on the HDR image only with coding distortion and the mixed distorted image after TM. Experimental results show that the objective prediction of the proposed method is better compared some representative method and more consistent with users’ visual perception.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 7549
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 9437
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Liu, Y., Yin, X., Wang, Y., Yin, Z., Zheng, Z.: HVS-based perception-driven no-reference omnidirectional image quality assessment. IEEE Trans. Instrum. Measur. 72, art no. 5003111 (2023)

    Google Scholar 

  2. Cao, L., You, J., Song, Y., Xu, H., Jiang, Z., Jiang, G.: Client-oriented blind quality metric for high dynamic range stereoscopic omnidirectional vision systems, Sensors 22, art no.8513 (2022)

    Google Scholar 

  3. Zhou, X., Zhang, Y., Li, N., Wang, X., Zhou, Y., Ho, Y.-S.: Projection invariant feature and visual saliency-based stereoscopic omnidirectional image quality assessment. IEEE Trans. Broadcast. 67(2), 512–523 (2021)

    Article  Google Scholar 

  4. Zhang, L., Zhang, L., Bovik, A.C.: A feature-enriched completely blind image quality evaluator. IEEE Trans. Image Process. 24(8), 2579–2591 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  5. Li, Q., Lin, W., Fang, Y.: No-reference quality assessment for multiply-distorted images in gradient domain. IEEE Sig. Process. Lett. 23(4), 541–545 (2016)

    Article  Google Scholar 

  6. Liu, L., Hua, Y., Zhao, Q., Huang, H., Bovik, A.C.: Blind image quality assessment by relative gradient statistics and adaboosting neural network. Sig. Process. Image Commun. 40, 1–15 (2016)

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  8. Gu, K., Zhai, G., Yang, X., Zhang, W.: Hybrid no-reference quality metric for singly and multiply distorted images. IEEE Trans. Broadcast. 60(3), 555–567 (2014)

    Article  Google Scholar 

  9. Ma, K., Liu, W., Liu, T., Wang, Z., Tao, D.: DipIQ: blind image quality assessment by learning-to-rank discriminable image pairs. IEEE Trans. Image Process. 26(8), 3951–3964 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  10. Min, X., Zhai, G., Gu, K., Liu, Y., Yang, X.: Blind image quality estimation via distortion aggravation. IEEE Trans. Broadcast. 64(2), 508–517 (2018)

    Article  Google Scholar 

  11. Liu, L., Liu, B., Su, C., Huang, H., Bovik, A.C.: Binocular spatial activity and reverse saliency driven no-reference stereopair quality assessment. Sig. Process. Image Commun. 58, 287–299 (2017)

    Article  Google Scholar 

  12. Shen, L., Chen, X., Pan, Z., Fan, K., Li, F., Lei, J.: No-reference stereoscopic image quality assessment based on global and local content characteristics. Neurocomputing 424, 132–142 (2021)

    Article  Google Scholar 

  13. Qi, Y., Jiang, G., Yu, M., Zhang, Y., Ho, Y.-S.: Viewport perception based blind stereoscopic omnidirectional image quality assessment. IEEE Trans. Circ. Syst. Video Technol. 31(10), 3926–3941 (2021)

    Article  Google Scholar 

  14. Gu, K., et al.: Blind quality assessment of tone-mapped images via analysis of information, naturalness, and structure. IEEE Trans. Multimedia 18(3), 432–443 (2016)

    Article  Google Scholar 

  15. Jiang, G., Song, H., Yu, M., Song, Y., Peng, Z.: Blind tone-mapped image quality assessment based on brightest/darkest regions, naturalness and aesthetics. IEEE Access 6, 2231–2240 (2018)

    Article  Google Scholar 

  16. Xu, J., et al.: STAR: A structure and texture aware Retinex model. IEEE Trans. Image Process. 29, 5022–5037 (2020)

    Article  MATH  Google Scholar 

  17. Chi, B., Yu, M., Jiang, G., He, Z., Peng, Z., Chen, F.: Blind tone mapped image quality assessment with image segmentation and visual perception, J. Vis. Commun. Image Represent. 67, art. no. 102752 (2020)

    Google Scholar 

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

    Article  Google Scholar 

  19. Yang, X., Ling, W., Lu, Z., Ong, E., Yao, S.: Just noticeable distortion model and its applications in video coding. Sig. Process. Image Commun. 20(7), 662–680 (2005)

    Article  Google Scholar 

  20. Wang, L., Zhang, C., Liu, Z., Sun, B.: Image feature detection based on phase congruency by Monogenic filters. In: Proceedings of theChinese Control and Decision Conference, pp. 2033–2038 (2014)

    Google Scholar 

  21. Farid, H., Simoncelli, E.P.: Differentiation of discrete multidimensional signals. IEEE Trans. Image Process. 13(4), 496–508 (2004)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61871247, 62071266 and 61931022, and Science and Technology Innovation 2025 Major Project of Ningbo (2022Z076).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gangyi Jiang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cao, L., Jiang, H., Jiang, Z., You, J., Yu, M., Jiang, G. (2023). Quality Assessment for High Dynamic Range Stereoscopic Omnidirectional Image System. In: Blanc-Talon, J., Delmas, P., Philips, W., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2023. Lecture Notes in Computer Science, vol 14124. Springer, Cham. https://doi.org/10.1007/978-3-031-45382-3_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-45382-3_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45381-6

  • Online ISBN: 978-3-031-45382-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics