An Online Platform for Underwater Image Quality Evaluation

  • Chau Yi LiEmail author
  • Riccardo Mazzon
  • Andrea Cavallaro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11188)


With the miniaturisation of underwater cameras, the volume of available underwater images has been considerably increasing. However, underwater images are degraded by the absorption and scattering of light in water. Image processing methods exist that aim to compensate for these degradations, but there are no standard quality evaluation measures or testing datasets for a systematic empirical comparison. For this reason, we propose PUIQE, an online platform for underwater image quality evaluation, which is inspired by other computer vision areas whose progress has been accelerated by evaluation platforms. PUIQE supports the comparison of methods through standard datasets and objective evaluation measures: quality scores for images uploaded on the platform are automatically computed and published in a leaderboard, which enables the ranking of methods. We hope that PUIQE will stimulate and facilitate the development of underwater image processing algorithms to improve underwater images.


Underwater image processing Evaluation platform Benchmark datasets Underwater image enhancement 


  1. 1.
    Horgan, J., Daniel T.: Computer Vision Applications in the Navigation of Unmanned Underwater Vehicles. Underwater Vehicles (2012)Google Scholar
  2. 2.
    Strachan, N.: Recognition of fish species by colour and shape. Image Vis. Comput. 11, 2–10 (1993)CrossRefGoogle Scholar
  3. 3.
    Emberton, S., Chittka, L., Cavallaro, A.: Hierarchical rank-based veiling light estimation for underwater dehazing. In: British Machine Vision Conference (2015)Google Scholar
  4. 4.
    Emberton, S., Chittka, L., Cavallaro, A.: Underwater image and video dehazing with pure haze region segmentation. Comput. Vis. Image Underst. 168, 145–156 (2018)CrossRefGoogle Scholar
  5. 5.
    Galdran, A., Pardo, D., Picn, A., Alvarez-Gila, A.: Automatic red-channel underwater image restoration. J. Vis. Commun. Image Represent. 26, 132–145 (2015)CrossRefGoogle Scholar
  6. 6.
    Peng, Y., Cosman, P.: Underwater image restoration based on image blurriness and light absorption. IEEE Trans. Image Process. 26, 1579–1594 (2017)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Li, C. Y., Cavallaro, A.: Background light estimation for depth-dependent underwater image restoration. In: IEEE International Conference on Image Processing (2018)Google Scholar
  8. 8.
    Ancuti, C., Ancuti, C., De Vleeschouwer, C., Bekaert, P.: Color balance and fusion for underwater image enhancement. IEEE Trans. Image Process. 27, 379–393 (2018)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Berman, D., Treibitz, T., Avidan, S.: Diving into haze-lines: color restoration of underwater images. In: British Machine Vision Conference (2017)Google Scholar
  10. 10.
    Chiang, J., Chen, Y.: Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans. Image Process. 21, 1756–1769 (2012)MathSciNetCrossRefGoogle Scholar
  11. 11.
    The Middlebury Computer Vision Pages. Accessed June 2018
  12. 12.
    Multiple Object Tracking Benchmark. Accessed June 2018
  13. 13.
    Yang, M., Sowmya, A.: An underwater color image quality evaluation metric. IEEE Trans. Image Process. 24, 6062–6071 (2015)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Panetta, K., Gao, C., Agaian, S.: Human-visual-system-inspired underwater image quality measures. IEEE J. Oceanic Eng. 41, 541–551 (2016)CrossRefGoogle Scholar
  15. 15.
    Hasler, D., Suesstrunk, S.: Measuring colorfulness in natural images. In: SPIE, pp. 87–95 (2003)Google Scholar
  16. 16.
    Panetta, K., Agaian, S., Zhou, Y., Wharton, E.: Parameterized logarithmic framework for image enhancement. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 41, 460–473 (2011)CrossRefGoogle Scholar
  17. 17.
    BubbleVision Youtube Channel. Accessed June 2018
  18. 18.
    Bins, J., Draper, B., Bohm, W., Najjar, W.: Precision vs. error in JPEG compression. In: SPIE, vol. 3817 (1999)Google Scholar
  19. 19.
    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2341–2353 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chau Yi Li
    • 1
    Email author
  • Riccardo Mazzon
    • 1
  • Andrea Cavallaro
    • 1
  1. 1.Centre for Intelligent SensingQueen Mary University of LondonLondonUK

Personalised recommendations