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Year 2021, Volume: 25 Issue: 3, 741 - 750, 30.06.2021
https://doi.org/10.16984/saufenbilder.760892

Abstract

References

  • [1] E. S. Gupta, and Y. Kaur, “Review of different histogram equalization based contrast enhancement techniques,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 3, no. 7, 2014
  • [2] C. H. Hsia, T. C. Wu, and J. S. Chiang, “A new method of moving object detection using adaptive filter,” Journal of Real-Time Image Processing, vol. 13, no. 2, pp. 311-325, 2017.
  • [3] M. Szczepanski, “Fast spatio-temporal digital paths video filter,” Journal of Real-Time Image Processing, vol. 16, no. 2, pp. 477-489, 2019.
  • [4] J. Jasmine, and S. Annadurai, “Real time video image enhancement approach using particle swarm optimization technique with adaptive cumulative distribution function based histogram equalization,” Measurement, vol. 145, pp. 833-840, 2019.
  • [5] H. Okuhata, K. Takahashi, Y. Nozato, T. Onoye, and I. Shirakawa, “Video image enhancement scheme for high resolution consumer devices,” In 2008 3rd International Symposium on Communications, Control and Signal Processing, pp. 639-644, Mar. 2008.
  • [6] X. Tan, Y. Liu, C. Zuo, and M. Zhang, “A real-time video denoising algorithm with FPGA implementation for Poisson–Gaussian noise,” Journal of Real-Time Image Processing, vol. 13, no. 2, pp. 327-343, 2017.
  • [7] G. Anbarjafari, S. Izadpanahi, and H. Demirel, “Video resolution enhancement by using discrete and stationary wavelet transforms with illumination compensation,” Signal, Image and Video Processing, vol. 9, no. 1, pp. 87-92, 2015.
  • [8] P. Singh, R. Mukundan, and R. De Ryke, “Feature Enhancement in Medical Ultrasound Videos Using Contrast- Limited Adaptive Histogram Equalization,” Journal of Digital Imaging, pp. 1-13, 2019.
  • [9] P. S. Altares, A. R. I. Copo, Y. A. Gabuyo, A. T. Laddaran, L. D. P. Mejia, I. A. Policapio, E. A. G. Sy, H. D. Tizon, and A. M. S. D. Yao, “Elementary statistics: a modern approach,” Rex Bookstore Inc., Manila, Philippines, 2003.
  • [10] C. F. Lee, J. C. Lee, and A. C. Lee, “Statistics for business and financial economics,” Singapore: World Scientific, 2000.
  • [11] N. Bajpai, “Business statistics,” Pearson Education India, 2009.
  • [12] P. Singh, and R. Shree, “A comparative study to noise models and image restoration techniques,” Int. J. Comput. Appl., vol. 149, no. 1, pp. 18-27, 2016.
  • [13] P. Shivakumara, W. Huang, and C. L. Tan, “An efficient edge based technique for text detection in video frames,” The Eighth IAPR International Workshop on Document Analysis Systems, pp. 307-314, Sept. 2008.
  • [14] P. Shivakumara, W. Huang, T. Q. Phan, and C. L. Tan, “Accurate video text detection through classification of low and high contrast images,” Pattern Recognition, vol. 43, no. 6, pp. 2165-2185, 2010.
  • [15] H. Li, W. Lei, W. Zhang, and Y. Guan, “A joint optimization method of coding and transmission for conversational HD video service,” Computer Communications, vol. 145, pp. 243-262, 2019.

Comparison of Statistical Methods for Obtaining Image from Video Frames Based on Development of Quality Metric

Year 2021, Volume: 25 Issue: 3, 741 - 750, 30.06.2021
https://doi.org/10.16984/saufenbilder.760892

Abstract

Digital images obtained from the video frames have an important role in different areas. Many image processing techniques have been applied to these images for different purposes such as edge detection. For a better image processing application, it is very important to obtain images with less oscillation from the video. Depending on the camera and environmental conditions, there are differences between consecutive frames. This means images with oscillation. Multiple frames can be used with different statistical methods to obtain images with less oscillation. In this paper, we developed a quality metric to compare the frames or images according to the amount of oscillation. Then a comparative study of statistical methods used to obtain the images with less oscillation from the video frames was presented. Images were obtained using four statistical methods for the different numbers of frames. This study also aims to evaluate how the choice of statistical method affects the oscillation of images using the developed quality metric and to compare the processing times of the methods.

References

  • [1] E. S. Gupta, and Y. Kaur, “Review of different histogram equalization based contrast enhancement techniques,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 3, no. 7, 2014
  • [2] C. H. Hsia, T. C. Wu, and J. S. Chiang, “A new method of moving object detection using adaptive filter,” Journal of Real-Time Image Processing, vol. 13, no. 2, pp. 311-325, 2017.
  • [3] M. Szczepanski, “Fast spatio-temporal digital paths video filter,” Journal of Real-Time Image Processing, vol. 16, no. 2, pp. 477-489, 2019.
  • [4] J. Jasmine, and S. Annadurai, “Real time video image enhancement approach using particle swarm optimization technique with adaptive cumulative distribution function based histogram equalization,” Measurement, vol. 145, pp. 833-840, 2019.
  • [5] H. Okuhata, K. Takahashi, Y. Nozato, T. Onoye, and I. Shirakawa, “Video image enhancement scheme for high resolution consumer devices,” In 2008 3rd International Symposium on Communications, Control and Signal Processing, pp. 639-644, Mar. 2008.
  • [6] X. Tan, Y. Liu, C. Zuo, and M. Zhang, “A real-time video denoising algorithm with FPGA implementation for Poisson–Gaussian noise,” Journal of Real-Time Image Processing, vol. 13, no. 2, pp. 327-343, 2017.
  • [7] G. Anbarjafari, S. Izadpanahi, and H. Demirel, “Video resolution enhancement by using discrete and stationary wavelet transforms with illumination compensation,” Signal, Image and Video Processing, vol. 9, no. 1, pp. 87-92, 2015.
  • [8] P. Singh, R. Mukundan, and R. De Ryke, “Feature Enhancement in Medical Ultrasound Videos Using Contrast- Limited Adaptive Histogram Equalization,” Journal of Digital Imaging, pp. 1-13, 2019.
  • [9] P. S. Altares, A. R. I. Copo, Y. A. Gabuyo, A. T. Laddaran, L. D. P. Mejia, I. A. Policapio, E. A. G. Sy, H. D. Tizon, and A. M. S. D. Yao, “Elementary statistics: a modern approach,” Rex Bookstore Inc., Manila, Philippines, 2003.
  • [10] C. F. Lee, J. C. Lee, and A. C. Lee, “Statistics for business and financial economics,” Singapore: World Scientific, 2000.
  • [11] N. Bajpai, “Business statistics,” Pearson Education India, 2009.
  • [12] P. Singh, and R. Shree, “A comparative study to noise models and image restoration techniques,” Int. J. Comput. Appl., vol. 149, no. 1, pp. 18-27, 2016.
  • [13] P. Shivakumara, W. Huang, and C. L. Tan, “An efficient edge based technique for text detection in video frames,” The Eighth IAPR International Workshop on Document Analysis Systems, pp. 307-314, Sept. 2008.
  • [14] P. Shivakumara, W. Huang, T. Q. Phan, and C. L. Tan, “Accurate video text detection through classification of low and high contrast images,” Pattern Recognition, vol. 43, no. 6, pp. 2165-2185, 2010.
  • [15] H. Li, W. Lei, W. Zhang, and Y. Guan, “A joint optimization method of coding and transmission for conversational HD video service,” Computer Communications, vol. 145, pp. 243-262, 2019.
There are 15 citations in total.

Details

Primary Language English
Subjects Electrical Engineering
Journal Section Research Articles
Authors

Murat Alparslan Güngör 0000-0001-7446-7808

Publication Date June 30, 2021
Submission Date June 30, 2020
Acceptance Date April 24, 2021
Published in Issue Year 2021 Volume: 25 Issue: 3

Cite

APA Güngör, M. A. (2021). Comparison of Statistical Methods for Obtaining Image from Video Frames Based on Development of Quality Metric. Sakarya University Journal of Science, 25(3), 741-750. https://doi.org/10.16984/saufenbilder.760892
AMA Güngör MA. Comparison of Statistical Methods for Obtaining Image from Video Frames Based on Development of Quality Metric. SAUJS. June 2021;25(3):741-750. doi:10.16984/saufenbilder.760892
Chicago Güngör, Murat Alparslan. “Comparison of Statistical Methods for Obtaining Image from Video Frames Based on Development of Quality Metric”. Sakarya University Journal of Science 25, no. 3 (June 2021): 741-50. https://doi.org/10.16984/saufenbilder.760892.
EndNote Güngör MA (June 1, 2021) Comparison of Statistical Methods for Obtaining Image from Video Frames Based on Development of Quality Metric. Sakarya University Journal of Science 25 3 741–750.
IEEE M. A. Güngör, “Comparison of Statistical Methods for Obtaining Image from Video Frames Based on Development of Quality Metric”, SAUJS, vol. 25, no. 3, pp. 741–750, 2021, doi: 10.16984/saufenbilder.760892.
ISNAD Güngör, Murat Alparslan. “Comparison of Statistical Methods for Obtaining Image from Video Frames Based on Development of Quality Metric”. Sakarya University Journal of Science 25/3 (June 2021), 741-750. https://doi.org/10.16984/saufenbilder.760892.
JAMA Güngör MA. Comparison of Statistical Methods for Obtaining Image from Video Frames Based on Development of Quality Metric. SAUJS. 2021;25:741–750.
MLA Güngör, Murat Alparslan. “Comparison of Statistical Methods for Obtaining Image from Video Frames Based on Development of Quality Metric”. Sakarya University Journal of Science, vol. 25, no. 3, 2021, pp. 741-50, doi:10.16984/saufenbilder.760892.
Vancouver Güngör MA. Comparison of Statistical Methods for Obtaining Image from Video Frames Based on Development of Quality Metric. SAUJS. 2021;25(3):741-50.

Sakarya University Journal of Science (SAUJS)