Skip to main content
SpringerLink
Log in

Histogram modification based enhancement along with contrast-changed image quality assessment

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Contrast is the difference in visual characteristics which make an object more recognizable. Despite the significance of contrast enhancement (CE) in image processing applications, few attempts have been made on assessment of the contrast change. In this paper, a visual information fidelity-based contrast change metric (VIF-CCM) is presented which includes visual information fidelity (VIF), local entropy, correlation coefficient, and mean intensity measures. The validation results of the presented VIF-CCM show its efficiency and superiority over the state-of–the-arts image quality assessment metrics. A histogram modification based contrast enhancement (HMCE) method is also proposed in this paper. The proposed HMCE comprises of four steps: segmentation of the input image, employing a set of weighting constraints, applying the combination of adaptive gamma correction and equalization on modified histogram, and optimization the value of the constraint weights by PSO algorithm. Experimental results demonstrate that the proposed HMCE outperforms other existing CE methods subjectively and objectively.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Chen S-D, Ramli AR (2003) Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans Consum Electron 49(4):1310–1319

    Article  Google Scholar 

  2. Chen S-D, Ramli AR (2003) Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans Consum Electron 49(4):1301–1309

    Article  Google Scholar 

  3. Chen Z, Jiang T, Tian Y (2014) Quality assessment for comparing image enhancement algorithms, in 2014 IEEE Conference on Computer Vision and Pattern Recognition, IEEE. p. 3003–3010.

  4. Cherifi D, Beghdadi A, Belbachir A (2010) Color contrast enhancement method using steerable pyramid transform. SIViP 4(2):247–262

    Article  MATH  Google Scholar 

  5. Dataset of Standard Grayscale Test Images (n.d.) Available from: http://decsai.ugr.es/cvg/CG/base.htm.

  6. Fang Y et al (2015) No-reference quality assessment of contrast-distorted images based on natural scene statistics. IEEE Signal Proc 22(7):838–842

    Google Scholar 

  7. Gao C, Panetta K, Agaian S (2012) A new color contrast enhancement algorithm for robotic applications, in 2012 IEEE International Conference on Technologies for Practical Robot Applications (TePRA). IEEE. p. 42–47

  8. Ghufran RS, Leu J-S, Prakosa SW (2018) Improving the age estimation accuracy by a hybrid optimization scheme. Multimed Tools Appl 77(2):2543–2559

    Article  Google Scholar 

  9. Gonzalez RC, Woods RE (2007) Digital image processing 3rd edition. Prentice Hall.

  10. Gu K, et al. (2013) Subjective and objective quality assessment for images with contrast change, in 2013 IEEE International Conference on Image Processing. IEEE. p. 383–387

  11. Hashemi S et al (2010) An image contrast enhancement method based on genetic algorithm. Pattern Recogn Lett 31(13):1816–1824

    Article  Google Scholar 

  12. Hautiere N et al (2008) Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal Stereol J 27(2):87–95

    Article  MathSciNet  MATH  Google Scholar 

  13. Huang S-C, Cheng F-C, Chiu Y-S (2013) Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Trans Image Process 22(3):1032–1041

    Article  MathSciNet  MATH  Google Scholar 

  14. Kennedy J, Eberhart R (1995) Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks, p. 1942-1948

  15. Kim Y-T (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans Consum Electron 43(1):1–8

    Article  MathSciNet  Google Scholar 

  16. Kim M, Chung MG (2008) Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement. IEEE Trans Consum Electron 54(3):1389–1397

    Article  Google Scholar 

  17. Kodak Lossless True Color Image Suite (n.d.) Available from: http://r0k.us/graphics/kodak.

  18. Kwok NM et al (2009) Contrast enhancement and intensity preservation for gray-level images using multiobjective particle swarm optimization. IEEE Trans Autom Sci Eng 6(1):145–155

    Article  Google Scholar 

  19. Larson EC, Chandler DM (2010) Most apparent distortion: full-reference image quality assessment and the role of strategy. J Electron Imaging 19(1):011006–011006-21

    Article  Google Scholar 

  20. Lin W, Kuo C-CJ (2011) Perceptual visual quality metrics: a survey. J Vis Commun Image Represent 22(4):297–312

    Article  Google Scholar 

  21. Liu L et al (2014) No-reference image quality assessment based on spatial and spectral entropies. Signal Process Image Commun 29(8):856–863

    Article  Google Scholar 

  22. Maini R, Aggarwal H (2010) A comprehensive review of image enhancement techniques. arXiv preprint arXiv:1003.4053

  23. Makhlouf Y, Daamouche A (2019) Automatic generation of adaptive structuring elements for road identification in VHR images. Expert Syst Appl 119:342–349

    Article  Google Scholar 

  24. Menotti D et al (2007) Multi-histogram equalization methods for contrast enhancement and brightness preserving. IEEE Trans Consum Electron 53(3):1186–1194

    Article  Google Scholar 

  25. Munteanu C, Rosa A (2004) Gray-scale image enhancement as an automatic process driven by evolution. IEEE Trans Syst Man Cybern B 34(2):1292–1298

    Article  Google Scholar 

  26. Padmanabhan SA, Kanchikere J (2019) An efficient face recognition system based on hybrid optimized KELM. Multimed Tools Appl:1–21

  27. Ponomarenko N et al (2009) TID2008-a database for evaluation of full-reference visual quality assessment metrics. Adv Mod Radioelectron 10(4):30–45

    Google Scholar 

  28. Ponomarenko N et al (2015) Image database TID2013: peculiarities, results and perspectives. Signal Process Image Commun 30:57–77

    Article  Google Scholar 

  29. Pratt WK 2001) Digital Image Processing: PIKS Inside. Wiley 758

  30. Rohaly AM et al (2000) Video quality experts group: current results and future directions, in visual communications and image processing. Int Soc Opt Photonics:742–753

  31. Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15(2):430–444

    Article  Google Scholar 

  32. Sheikh HR, Sabir MF, Bovik AC (2006) A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans Image Process 15(11):3440–3451

    Article  Google Scholar 

  33. Shih FY et al (2018) An adjustable-purpose image watermarking technique by particle swarm optimization. Multimed Tools Appl 77(2):1623–1642

    Article  Google Scholar 

  34. Shokrollahi A, Mahmoudi-Aznaveh A, Maybodi BM-N (2017) Image quality assessment for contrast enhancement evaluation. AEU Int J Electron Commun 77:61–66

    Article  Google Scholar 

  35. Simone G, Pedersen M, Hardeberg JY (2012) Measuring perceptual contrast in digital images. J Vis Commun Image Represent 23(3):491–506

    Article  Google Scholar 

  36. Sivaranjani R, Roomi SMM, Senthilarasi M (2019) Speckle noise removal in SAR images using multi-objective PSO (MOPSO) algorithm. Appl Soft Comput

  37. Sporring J (1996) The entropy of scale-space, in Proceedings of 13th International Conference on Pattern Recognition. IEEE. p. 900–904.

  38. Starck J-L, Murtagh F, Candès EJ, Donoho DL (2003) Gray and color image contrast enhancement by the curvelet transform. IEEE Trans Image Process 12(6):706–717

    Article  MathSciNet  MATH  Google Scholar 

  39. The Berkeley Segmentation Dataset and Benchmark (n.d.). Available from: https://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/.

  40. The USC-SIPI Image Database (n.d.). Available from: http://sipi.usc.edu/database.

  41. Tobias OJ, Seara R (2002) Image segmentation by histogram thresholding using fuzzy sets. IEEE Trans Image Process 11(12):1457–1465

    Article  Google Scholar 

  42. Velde KV (1999) Multi-scale color image enhancement, in Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348). IEEE. p. 584–587

  43. Wang Q, Ward RK (2007) Fast image/video contrast enhancement based on weighted thresholded histogram equalization. IEEE Trans Consum Electron 53(2):757–764

    Article  Google Scholar 

  44. Wang C, Ye Z (2005) Brightness preserving histogram equalization with maximum entropy: a variational perspective. IEEE Trans Consum Electron 51(4):1326–1334

    Article  Google Scholar 

  45. Wang Y, Chen Q, Zhang B (1999) Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Trans Consum Electron 45(1):68–75

    Article  Google Scholar 

  46. Wang Z et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  47. Wu X (2010) A linear programming approach for optimal contrast-tone mapping. IEEE Trans Image Process 20(5):1262–1272

    MathSciNet  MATH  Google Scholar 

  48. Zhang L et al (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386

    Article  MathSciNet  MATH  Google Scholar 

  49. Zhu H et al (2017) Segmentation of liver cyst in ultrasound image based on adaptive threshold algorithm and particle swarm optimization. Multimed Tools Appl 76(6):8951–8968

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Islamic Republic of Iran

    Ayub Shokrollahi & Babak Mazloom-Nezhad Maybodi

  2. Cyberspace Research Institute, Shahid Beheshti University, Tehran, Islamic Republic of Iran

    Ahmad Mahmoudi-Aznaveh

Authors
  1. Ayub Shokrollahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Babak Mazloom-Nezhad Maybodi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmad Mahmoudi-Aznaveh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmad Mahmoudi-Aznaveh.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shokrollahi, A., Mazloom-Nezhad Maybodi, B. & Mahmoudi-Aznaveh, A. Histogram modification based enhancement along with contrast-changed image quality assessment. Multimed Tools Appl 79, 19193–19214 (2020). https://doi.org/10.1007/s11042-020-08830-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-08830-9

Keywords

Search

Navigation