Quality Assessment of Compressed Video for Automatic License Plate Recognition

Anna Ukhanova, Jesper Støttrup-Andersen, Søren Forchhammer, John Madsen

2014

Abstract

Definition of video quality requirements for video surveillance poses new questions in the area of quality assessment. This paper presents a quality assessment experiment for an automatic license plate recognition scenario. We explore the influence of the compression by H.264/AVC and H.265/HEVC standards on the recognition performance. We compare logarithmic and logistic functions for quality modeling. Our results show that a logistic function can better describe the dependence of recognition performance on the quality for both compression standards. We observe that automatic license plate recognition in our study has a behavior similar to human recognition, allowing the use of the same mathematical models. We furthermore propose an application of one of the models for video surveillance systems.

References

  1. Dumke, J., Ford, C., and Stange, I. (2011). The effects of scene characteristics, resolution, and compression on the ability to recognize objects in video. Human Vision and Electronic Imaging XVI, Proc. of SPIE-IS&T Electronic Imaging.
  2. Ford, C. and Stange, I. (2010). A framework for generalizing public safety video applications to determine quality requirements. Multimedia Communications, Services and Security.
  3. H.264/AVC codec. Free software library x264. http://www.videolan.org/developers/x264.html.
  4. H.265/HEVC codec. Reference software HM, version 9.2. http://hevc.hhi.fraunhofer.de/.
  5. Janowski, L., Kozlowski, P., Baran, R., Romaniak, P., Glowacz, A., and Rusc, T. (2012). Quality assessment for a visual and automatic license plate recognition. Multimedia Tools and Applications.
  6. K. Jung, K. I. Kim, A. K. J. (2004). Text information extraction in images and video: a survey. Pattern Recognition, 37(5):977-997.
  7. Leszczuk, M. (2011). Assessing task-based video quality - a journey from subjective psycho-physical experiments to objective quality models. Multimedia Communications, Services and Security.
  8. Leszczuk, M. (2012). Optimising task-based video quality - a journey from subjective psychophysical experiments to objective quality optimisation. Multimedia Tools and Applications, pages 1-18.
  9. Leszczuk, M. and Dumke, J. (2012). Quality assessment for recognition tasks (QART). EMERGING 2012, The Fourth International Conference on Emerging Network Intelligence, pages 69-73.
  10. Leszczuk, M., Janowski, L., Romaniak, P., Glowacz, A., and Mirek, R. (2011a). Quality assessment for a licence plate recognition task based on a video streamed in limited networking conditions. Multimedia Communications, Services and Security, pages 10-18.
  11. Leszczuk, M., Stange, I., and Ford, C. (2011b). Determining image quality requirements for recognition tasks in generalized public safety video applications: Definitions, testing, standardization, and current trends. IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), pages 1-5.
  12. Pinson, M. and Wolf, S. (2004). A new standardized method for objectively measuring video quality. IEEE Transactions on Broadcasting, 50(3):312-322.
  13. Seshadrinathan, K. and Bovik, A. (2010). Motion tuned spatio-temporal quality assessment of natural videos. IEEE Transactions on Image Processing, 19(2).
  14. Sullivan, G. J., Ohm, J.-R., Han, W.-J., and Wiegand, T. (2012). Overview of the High Efficiency Video Coding (HEVC) standard. IEEE Transactions on Circuits and Systems for Video Technology, 22(12).
  15. Video Quality in Public Safety Working Group (2010). Defining video quality requirements: A guide for public safety, volume 1.0.
  16. Wang, Z., Bovik, A., Sheikh, H., and Simoncelli, E. (2004). Image quality assessment: From error measurement to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612.
  17. Wiegand, T., Sullivan, G. J., Bjontegaard, G., and Luthra, A. (2003). Overview of the H.264/AVC video coding standard. IEEE Transactions on Circuits and Systems for Video Technology, 13(7).
  18. Witkowski, M. and Leszczuk, M. (2012). Classification of video sequences into specified generalized use classes of target size and lighting level. IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), pages 1-5.
Download


Paper Citation


in Harvard Style

Ukhanova A., Støttrup-Andersen J., Forchhammer S. and Madsen J. (2014). Quality Assessment of Compressed Video for Automatic License Plate Recognition . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-009-3, pages 306-313. DOI: 10.5220/0004671203060313


in Bibtex Style

@conference{visapp14,
author={Anna Ukhanova and Jesper Støttrup-Andersen and Søren Forchhammer and John Madsen},
title={Quality Assessment of Compressed Video for Automatic License Plate Recognition},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={306-313},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004671203060313},
isbn={978-989-758-009-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)
TI - Quality Assessment of Compressed Video for Automatic License Plate Recognition
SN - 978-989-758-009-3
AU - Ukhanova A.
AU - Støttrup-Andersen J.
AU - Forchhammer S.
AU - Madsen J.
PY - 2014
SP - 306
EP - 313
DO - 10.5220/0004671203060313