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Method for Assessing Objective Video Quality for Automatic License Plate Recognition Tasks

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Multimedia Communications, Services and Security (MCSS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1689))

Abstract

In the modern era, there are numerous metrics for overall Quality of Experience (QoE), both those with Full Reference (FR), such as Peak Signal-to-Noise Ratio (PSNR) or Structural Similarity (SSIM), and those with No Reference (NR), such as Video Quality Indicators (VQI), that are successfully used in video processing systems to assess videos whose quality is diminished by various processing scenarios. However, they are not appropriate for video sequences used for jobs that require recognition (Target Recognition Videos, TRV). As a result, a significant research problem remains to accurately assess the performance of the video processing pipeline in both human and Computer Vision (CV) recognition tasks. For recognition tasks, there is a need for objective ways to assess video quality. In this research, we demonstrate that it is feasible to create a novel idea of an objective model to assess video quality for automatic licence plate recognition (ALPR) tasks in response to this demand. A representative set of image sequences is used to train, test, and validate the model. The collection of degradation scenarios is based on a digital camera model and how a scene’s luminous flux eventually transforms into a digital image. The generated degraded images are evaluated for ALPR and VQI using a CV library. The value of the F-measure parameter of 0.777 represents the measured accuracy of a model.

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Notes

  1. 1.

    The meaning of headers in columns is explained in: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html.

References

  1. FFmpeg: FFmpeg (2019). https://ffmpeg.org/. Accessed 04 June 2019

  2. Garcia-Zapirain, B., et al.: A proposed methodology for subjective evaluation of video and text summarization. In: Choroś, K., Kopel, M., Kukla, E., Siemiński, A. (eds.) MISSI 2018. AISC, vol. 833, pp. 396–404. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-98678-4_40

    Chapter  Google Scholar 

  3. Ghadiyaram, D., Bovik, A.C.: Perceptual quality prediction on authentically distorted images using a bag of features approach. J. Vis. 17(1), 32 (2017)

    Article  Google Scholar 

  4. Grega, M., et al.: An integrated AMIS prototype for automated summarization and translation of newscasts and reports. In: Choroś, K., Kopel, M., Kukla, E., Siemiński, A. (eds.) MISSI 2018. AISC, vol. 833, pp. 415–423. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-98678-4_42

    Chapter  Google Scholar 

  5. Hofbauer, H., Autrusseau, F., Uhl, A.: To recognize or not to recognize-a database of encrypted images with subjective recognition ground truth. Inf. Sci. 551, 128–145 (2021)

    Article  MathSciNet  Google Scholar 

  6. ImageMagick Studio LLC: ImageMagick: Convert, Edit, Or Compose Bitmap Images (2011). https://imagemagick.org/index.php, https://www.imagemagick.org/script/index.php

  7. Janowski, L., Papir, Z.: Modeling subjective tests of quality of experience with a generalized linear model. In: 2009 International Workshop on Quality of Multimedia Experience, pp. 35–40. IEEE (2009)

    Google Scholar 

  8. Kawa, K., Leszczuk, M., Boev, A.: Survey on the state-of-the-art methods for objective video quality assessment in recognition tasks. In: Dziech, A., Mees, W., Czyżewski, A. (eds.) MCSS 2020. CCIS, vol. 1284, pp. 332–350. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59000-0_25

    Chapter  Google Scholar 

  9. Khan, Z.A., et al.: Towards a video quality assessment based framework for enhancement of laparoscopic videos. In: Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, vol. 11316, p. 113160P. International Society for Optics and Photonics (2020)

    Google Scholar 

  10. Leszczuk, M.: Assessing task-based video quality—a journey from subjective psycho-physical experiments to objective quality models. In: Dziech, A., Czyżewski, A. (eds.) MCSS 2011. CCIS, vol. 149, pp. 91–99. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21512-4_11

    Chapter  Google Scholar 

  11. Leszczuk, M.: Revising and improving the ITU-T recommendation p. 912. J. Telecommun. Inf. Technol. (2015)

    Google Scholar 

  12. Leszczuk, M., Hanusiak, M., Farias, M.C., Wyckens, E., Heston, G.: Recent developments in visual quality monitoring by key performance indicators. Multimed. Tools Appl. 75(17), 10745–10767 (2016)

    Article  Google Scholar 

  13. Leszczuk, M., Janowski, L.: Selected aspects of the new recommendation on subjective methods of assessing video quality in recognition tasks. In: Paszkiel, S. (ed.) ICBCI 2021. AISC, vol. 1362, pp. 246–254. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72254-8_27

    Chapter  Google Scholar 

  14. Leszczuk, M., Janowski, L., Nawała, J., Boev, A.: Objective video quality assessment method for face recognition tasks. Electronics 11(8), 1167 (2022)

    Article  Google Scholar 

  15. Leszczuk, M., Janowski, L., Romaniak, P., Głowacz, A., Mirek, R.: Quality assessment for a licence plate recognition task based on a video streamed in limited networking conditions. In: Dziech, A., Czyżewski, A. (eds.) MCSS 2011. CCIS, vol. 149, pp. 10–18. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21512-4_2

    Chapter  Google Scholar 

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

    Google Scholar 

  17. Mahankali, N.S., Raghavan, M., Channappayya, S.S.: No-reference video quality assessment using voxel-wise fMRI models of the visual cortex. IEEE Signal Process. Lett. 29, 319–323 (2021)

    Article  Google Scholar 

  18. 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  Google Scholar 

  19. Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind’’ image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2012)

    Article  Google Scholar 

  20. Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Process. Lett. 17(5), 513–516 (2010)

    Article  Google Scholar 

  21. Mu, M., Romaniak, P., Mauthe, A., Leszczuk, M., Janowski, L., Cerqueira, E.: Framework for the integrated video quality assessment. Multimed. Tools Appl. 61(3), 787–817 (2012)

    Article  Google Scholar 

  22. Oszust, M.: Local feature descriptor and derivative filters for blind image quality assessment. IEEE Signal Process. Lett. 26(2), 322–326 (2019)

    Article  Google Scholar 

  23. Romaniak, P., Janowski, L., Leszczuk, M., Papir, Z.: Perceptual quality assessment for H. 264/AVC compression. In: 2012 IEEE consumer communications and networking conference (CCNC), pp. 597–602. IEEE (2012)

    Google Scholar 

  24. Shi, H., Liu, C.: An innovative video quality assessment method and an impairment video dataset. In: 2021 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 1–6. IEEE (2021)

    Google Scholar 

  25. Wu, J., Ma, J., Liang, F., Dong, W., Shi, G., Lin, W.: End-to-end blind image quality prediction with cascaded deep neural network. IEEE Trans. Image Process. 29, 7414–7426 (2020)

    Article  Google Scholar 

  26. Xing, W., et al.: Recognition and classification of single melt tracks using deep neural network: a fast and effective method to determine process windows in selective laser melting. J. Manuf. Process. 68, 1746–1757 (2021)

    Article  Google Scholar 

  27. Xu, J., Ye, P., Li, Q., Du, H., Liu, Y., Doermann, D.: Blind image quality assessment based on high order statistics aggregation. IEEE Trans. Image Process. 25(9), 4444–4457 (2016)

    Article  MathSciNet  Google Scholar 

  28. Ye, P., Kumar, J., Kang, L., Doermann, D.: Unsupervised feature learning framework for no-reference image quality assessment. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1098–1105. IEEE (2012)

    Google Scholar 

  29. 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  Google Scholar 

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Acknowledgements

This research received funding from the Huawei Innovation Research Programme (HIRP).

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Authors and Affiliations

  1. AGH University of Science and Technology, Kraków, Poland

    Mikołaj Leszczuk, Lucjan Janowski & Jakub Nawała

  2. Huawei Technologies Dusseldorf GmbH, Düsseldorf, Germany

    Atanas Boev

Authors
  1. Mikołaj Leszczuk
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  2. Lucjan Janowski
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  3. Jakub Nawała
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  4. Atanas Boev
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Corresponding author

Correspondence to Mikołaj Leszczuk .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Kraków, Poland

    Andrzej Dziech

  2. Royal Military Academy, Brussels, Belgium

    Wim Mees

  3. AGH University of Science and Technology, Kraków, Poland

    Marcin Niemiec

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Leszczuk, M., Janowski, L., Nawała, J., Boev, A. (2022). Method for Assessing Objective Video Quality for Automatic License Plate Recognition Tasks. In: Dziech, A., Mees, W., Niemiec, M. (eds) Multimedia Communications, Services and Security. MCSS 2022. Communications in Computer and Information Science, vol 1689. Springer, Cham. https://doi.org/10.1007/978-3-031-20215-5_13

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  • DOI: https://doi.org/10.1007/978-3-031-20215-5_13

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  • Online ISBN: 978-3-031-20215-5

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