Skip to main content
SpringerLink
Log in

Novel Quality Metric for Image Super Resolution Algorithms - Super Resolution Entropy Metric (SREM)

  • Conference paper
  • First Online:
Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1035))

Abstract

Even with the topical developments of numerous image Super Resolution (SR) algorithms, how to quantify the visual quality scores of a super resolved image is still an open research problem. Majority of SR images are evaluated by full-reference metric with the support of a reference image. There are some circumstances when a reference image is unavailable or is with degraded quality. We propose a super resolution benchmark Super Resolution Entropy Metric (SREM) which can be used to evaluate the effectiveness of pixel reconstruction and quality of the image in the absence of reference image automatically. SREM measures the experimental quality of an SR image based on the perceptions of acutance and spatial discontinuity features in the gradient domain and wavelet domain. Experimental scores illustrate that the SREM metric is competent for assessing the visual quality of super-resolved images.

Supported by DST PURSE Phase (II), Govt. of India.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • 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

References

  1. Sun, J., Xu, Z., Shum, H.: Image super-resolution using gradient profile prior. In: CVPR (2008)

    Google Scholar 

  2. Kim, K.I., Kwon, Y.: Single-image super-resolution using sparse regression and natural image prior. IEEE Trans. Pattern Anal. Mach. Intell 32(6), 1127–1133 (2010). https://doi.org/10.1109/TPAMI.2010.25

    Article  Google Scholar 

  3. Shan, Q., Li, Z., Jia, J., Tang, C.-K.: Fast image/video upsampling. ACM Trans. Graph 27(5), 153 (2008). https://doi.org/10.1145/1409060.1409106

    Article  Google Scholar 

  4. Rani, M., Peleg, S.: Improving resolution by image registration. CVGIP 53(3), 231–239 (1991)

    Google Scholar 

  5. Timofte, R., De Smet, V., Van Gool, L.: A+: adjusted anchored neighborhood regression for fast super-resolution. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9006, pp. 111–126. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16817-3_8

    Chapter  Google Scholar 

  6. Freeman, W.T., Johnes, T.R., Pasztor, E.C.: Example-based super- resolution. IEEE Comput. Graph. Appl 27(2), 56–65 (2002). https://doi.org/10.1109/38.988747

    Article  Google Scholar 

  7. Farsiu, S., Robinson, M.D., Elad, M., Milanfar, P.: Advances and challenges in super-resolution. Int. J. Imaging Syst. Technol 14(2), 47–57 (2004)

    Article  Google Scholar 

  8. Yang, J.C., Wright, J., Huang, T., Ma, Y.: Image super-resolution via sparse representation representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010). https://doi.org/10.1109/TIP.2010.2050625

    Article  MathSciNet  MATH  Google Scholar 

  9. Yang, J.C., Lin, Z., Cohen, S.: Fast image super-resolution based on in-place example regression. In: IEEE Conference on Computer Vision and Pattern Recognition, Portland, pp. 1059–1066 (2013). https://doi.org/10.1109/CVPR.2013.141

  10. Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_13

    Chapter  Google Scholar 

  11. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). https://doi.org/10.1109/TIP.2003.819861

    Article  Google Scholar 

  12. Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011). https://doi.org/10.1109/TIP.2011.2109730

    Article  MathSciNet  MATH  Google Scholar 

  13. Reibman, A.R., Bell, R., Gray, M.: Quality assessment for super-resolution image enhancement. In: ICIP (2006)

    Google Scholar 

  14. Yeganeh, H., Rostami, M., Wang, Z.: Objective quality assessment for image super-resolution: a natural scene statistics approach. In: ICIP (2012)

    Google Scholar 

  15. Tang, H., Joshi, N., Kapoor, A.: Blind image quality assessment using semi-supervised rectifier networks. In: CVPR (2014)

    Google Scholar 

  16. 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). https://doi.org/10.1109/TIP.2012.2214050

    Article  MathSciNet  MATH  Google Scholar 

  17. Saad, M.A., Bovik, A.C., Charrier, C.: Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans. Image Process. 21(8), 3339–3352 (2012). https://doi.org/10.1109/TIP.2012.2191563

    Article  MathSciNet  MATH  Google Scholar 

  18. Moorthy, A.K., Bovik, A.C.: Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Trans. Image Process. 20(12), 350–3364 (2011). https://doi.org/10.1109/TIP.2011.2147325

    Article  MathSciNet  MATH  Google Scholar 

  19. Candemir, S., Borovikov, E., Santosh, K.C., Antani, S., Thoma, G.: RSILC: rotation- and scale-invariant, line-based color-aware descriptor. Image Vis. Comput. 42, 1–12 (2015). https://doi.org/10.1016/j.imavis.2015.06.010

    Article  Google Scholar 

  20. Santosh, K.C., Aafaque, A., Antani, S., Thoma, G.R.: Line segment-based stitched multipanel figure separation for effective biomedical CBIR. Int. J. Pattern Recognit. Artif. Intell. 31(5) (2017). https://doi.org/10.1142/S0218001417570038

    Article  Google Scholar 

  21. Santosh, K.C., Wendling, L., Antani, S., Thoma, G.R.: Overlaid arrow detection for labeling regions of interest in biomedical images. IEEE Intell. Syst. 31(3), 66–75 (2016). https://doi.org/10.1109/MIS.2016.24

    Article  Google Scholar 

  22. Elder, J.H.: Are edges incomplete. Int. J. Comput. Vision 34(2–3), 97–122 (1999)

    Article  Google Scholar 

  23. Greeshma, M.S., Bindu, V.R.: Single image super resolution using fuzzy deep convolutional networks. In: IEEE International Conference on Technological Advancements in Power and Energy, Kollam, pp. 1–6 (2017)

    Google Scholar 

  24. Reenu, M., David, D., Raj, S.A., Nair, M.S.: Sharp features (WASH): an image quality assessment metric based on HVS Wavelet based. In: Second International Conference on Advanced Computing, Networking and Security, pp. 79–83 (2013)

    Google Scholar 

  25. Kodak Lossless True Colour Image Suite. http://r0k.us/graphics/kodak/. Accessed July 2018

Download references

Acknowledgement

Authors acknowledge the support extended by DST-PURSE Phase II, Govt of India.

Author information

Authors and Affiliations

  1. School of Computer Sciences, Mahatma Gandhi University, Kottayam, Kerala, India

    M. S. Greeshma & V. R. Bindu

Authors
  1. M. S. Greeshma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. R. Bindu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to M. S. Greeshma or V. R. Bindu .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Greeshma, M.S., Bindu, V.R. (2019). Novel Quality Metric for Image Super Resolution Algorithms - Super Resolution Entropy Metric (SREM). In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1035. Springer, Singapore. https://doi.org/10.1007/978-981-13-9181-1_14

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-9181-1_14

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9180-4

  • Online ISBN: 978-981-13-9181-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation