OBJECT QUALITY ENHANCEMENT OF MULTI-FRAME LOW-RESOLUTION VIDEO

Authors

  • Siti Aisyah Electrical Engineering Department, Politeknik Negeri Batam, Batam, Indonesia
  • Fitri Arnia Electrical Engineering Department, Syiah Kuala University, Aceh, Indonesia

DOI:

https://doi.org/10.11113/jt.v77.6658

Keywords:

Digital video, frame rate, interpolation, super-resolution, MOS

Abstract

A good quality image is required in various applications such as object identification and authentication. This research presents the performance of image resolution enhancement method, in which the low-resolution image originated from low-resolution CCTV video. The enhancement method is initialized by averaging video frames and continued by interpolating the resulted images using the existing interpolation techniques, namely bilinear, bi-cubic, nearest neighbor and spleen. Frame rate of 15 and 25 frames per second (fps) has been applied to the testing video. The result shows that the differences of frame rate and number of the averaged frame would affect image quality. Subjective assessment of respondents of MOS above 3 has been obtained by increasing the frame rate and the number of averaging frames.  

References

Poynton, C. 1996. A Technical Introduction to Digital Video. England: John Wiley & Sons Ltd.

Petrou, M. and Bosdogianni, P. 1999. Image Processing The Fundamentals, West Sussex, England: John Wiley & Sons Ltd.

Nailul Mustaqim Abdi, et al. 2011. Peningkatan Kualitas Citra Digital Menggunakan Metode Super Resolusi Pada Domain Spasial. Jurnal Rekayasa Elektrika. 9(3).

Shen Wang, et al., 2010. A Novel and Secure Image Interpolation Methods for Image Disguise. Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing. IEEE Computer Society. 482–485.

R.Swaminathan, and Manoj Wadhwa. 2014. Satellite Image enhancement using Combination of Transform Techniques and Interpolation Methods. International Journal of Engineering and Computer Science. 3: 5529-5532.

George J. Grevera, and Jayaram K. Udupa. 1998. An Objective Comparison of 3-D Image Interpolation Method. IEEE Transactions on Medical Imaging. 17(4).

Fei He1, et al. 2011. An Effective Method for Interpolation. 19th International Conference on Geoinformatics, Shanghai. 1-6.

Jing M. Chen, et. al. 2006. Locally Adjusted Cubic-Spline Capping for Reconstructing Seasonal Trajectories of a Satellite-Derived Surface Parameter. IEEE Transactions on Geoscience and Remote Sensing. 44(8).

Paolo Villa, and Marco Gianinetto. 2006. Multispectral Transform and Spline Interpolation for Mapping Flood Damages. IEEE International Conference on Geoscience and Remote Sensing Symposium, Denver CO. 275-278.

Philippe Thévenaz, Thierry Blu and Michael Unser. Image Interpolation and Resampling. Swiss Federal Institute of Technology—Lausanne.

Ranjeet Roy, Maninder Pal and Tarun Gulati. 2013. Zooming Digital Images using Interpolation. International Journal of Application or Innovation in Engineering & Management (IJAIEM). 2(4).

ITU-T P.800.1. 2006. Methods For Objective And Subjective Assessment Of Quality. Mean Opinion Score (MOS) terminology.

ITU-R BT.500-11. 2002. Methodology for The Subjective Assessment of The Quality of Television Pictures

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Published

2015-12-11

How to Cite

OBJECT QUALITY ENHANCEMENT OF MULTI-FRAME LOW-RESOLUTION VIDEO. (2015). Jurnal Teknologi (Sciences & Engineering), 77(22). https://doi.org/10.11113/jt.v77.6658