Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
권호 표지
DOI QR코드

DOI QR Code

Enhanced Local Directional Pattern based video shot boundary detection and automatic synchronization for STB quality inspection

STB 품질검사를 위한 개선된 지역 방향 패턴 기반 비디오 샷 경계 검출 및 자동 동기화

  • Cho, Youngtak (Dept. of Computer Science and Engineering, Kyung Hee University) ;
  • Chae, Oksam (Dept. of Computer Science and Engineering, Kyung Hee University)
  • 조영탁 (경희대학교 컴퓨터공학과) ;
  • 채옥삼 (경희대학교 컴퓨터공학과)
  • Received : 2019.01.30
  • Accepted : 2019.03.20
  • Published : 2019.03.28
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, the importance of pre-shipment quality inspection has been emphasized due to the increase of STB supply. In this paper, we propose a method to support automation of quality inspection through simultaneous multi-channel input of STB video signal. The proposed method extracts a fingerprint using the center scan line of the image after stable video shot boundary detection using CeLDP combining color information and LDP code and performs synchronization between input video channels. The proposed method shows stronger shot boundary detection performance than the conventional shot detection method. Through the experiments applied to the real environment, it is possible to secure reliability and real-time quality check for synchronization between multi-channel inputs required for STB quality inspection. Also, based on the proposed method, we intend to study a large-scale quality inspection method in the future and propose a more effective quality inspection system.

최근 STB 보급이 증가하면서 제품 출하 전 품질검사의 중요성이 부각되고 있다. 본 논문에서는 STB 영상 신호의 다채널 동시 입력을 통한 품질검사 자동화를 지원하기 위한 방법을 제안한다. 제안 방법은 먼저 색상 정보와 LDP 코드를 결합한 CeLDP를 이용하여 안정적인 비디오 샷 경계 검출 후 영상의 중앙 스캔라인을 이용한 핑거프린트를 추출하여 입력 비디오 채널 간 동기화를 수행한다. 제안하는 방법은 기존 샷 검출 방법과 비교를 통해 더욱 강인한 샷 경계 검출 성능을 보이는 것을 확인하였으며, 실제 환경에 적용한 실험을 통해 STB 품질검사 시 필요한 다채널 입력 간 동기화를 위한 신뢰성 확보 및 실시간 품질검사가 가능함을 입증하였다. 또, 제안된 방법을 바탕으로 향후 대규모 품질검사 방법을 연구하여 보다 효과적인 품질검사 체계를 제안하고자 한다.

Keywords

JKOHBZ_2019_v9n3_8_f0001.png 이미지

Fig. 1. Automated STB quality inspection system

JKOHBZ_2019_v9n3_8_f0002.png 이미지

Fig. 2. Example of STB EPG menu screen

JKOHBZ_2019_v9n3_8_f0003.png 이미지

Fig. 3. Example of assigning relative index is and it

JKOHBZ_2019_v9n3_8_f0004.png 이미지

Fig. 4. Color bits considered in the proposed method

JKOHBZ_2019_v9n3_8_f0005.png 이미지

Fig. 5. Final feature combined color information with eLDP code

JKOHBZ_2019_v9n3_8_f0006.png 이미지

Fig. 6. Shot-based fingerprint generation process

JKOHBZ_2019_v9n3_8_f0007.png 이미지

Fig. 7. Shot boundary detection result

JKOHBZ_2019_v9n3_8_f0008.png 이미지

Fig. 8. Fingerprints of reference and query video

JKOHBZ_2019_v9n3_8_f0009.png 이미지

Fig. 9. PSNR calculation result using fingerprint

JKOHBZ_2019_v9n3_8_f0010.png 이미지

Fig. 10. Monitoring UI of automated STB quality inspection system applying proposed method

Table 1. FDR comparison of proposed shot boundary detection method

JKOHBZ_2019_v9n3_8_t0001.png 이미지

References

  1. S. Y. Min, S. H. Park & N. H. Lee. (2011). SW Quality of Convergence Product: Characteristics, Improvement Strategies and Alternatives. Journal of Convergence for Information Technology, 1(1), 19-28.
  2. D. H. Kim, Y. J. Jung & J. E. Hong. (2016). Analysis of Refactoring Techniques and Tools for Source Code Quality Improvement. Journal of Convergence for Information Technology, 6(4), 137-150. https://doi.org/10.22156/CS4SMB.2016.6.4.137
  3. H. J. Jung. (2017). The Quantity Data Estimation for Software Quality Testing. Journal of the Korea Convergence Society, 8(10), 37-43. https://doi.org/10.15207/JKCS.2017.8.4.037
  4. D. H. Byun. (2012). Methodology for Measuring the Quality of Three-Dimensional Television. Journal of Digital Convergence, 10(4), 1-9. https://doi.org/10.14400/JDPM.2012.10.4.001
  5. Tae-Kyung Cho. (2015). The Study on the Performance Evaluation of IPTV according to the increase of network traffic on the Internet Environment. Journal of Digital Convergence, 13(11), 179-185. https://doi.org/10.14400/JDC.2015.13.11.179
  6. J. Bach. (2004). Exploratory testing. The testing practitioner, 253-265.
  7. D. H. Kim & Y. Kim. (2018, Feb). A New Exploratory Testing Method for Improving the Effective IP Set-Top Box Test. Journal of The Korea Society of Computer and Information, 23(2), 9-16. https://doi.org/10.9708/JKSCI.2018.23.02.009
  8. stb-tester. Automated Testing for Set-Top Boxes and Smart TVs, http://Stb-tester.com [Accessed 18 Jan. 2019].
  9. J. S. Boreczky & L. A. Rowe. (1996). Comparison of video shot boundary detection techniques. Journal of Electronic Imaging, 5(2), 122-128. https://doi.org/10.1117/12.238675
  10. J. Mas & G. Fernandez. (2003). Video shot boundary detection based on color histogram. Notebook Papers TRECVID2003, Gaithersburg, Maryland, NIST.
  11. A. Nagasaka & Y. Tanaka. (1992). Automatic video indexing and full-video search for object appearances. Journal of Information Processing, 15(2), 316.
  12. K. O. Ahn, et al. (2015). Video Shot Boundary Detection based on Color and LBP code for Remote Smart Collaboration. IEEK summer conference, pp. 593-596.
  13. T. Ahonen, A. Hadid & M. Pietikainen. (2006). Face description with local binary patterns: Application to face recognition. IEEE transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037-2041. https://doi.org/10.1109/TPAMI.2006.244
  14. T. Jabid, M. H. Kabir & O. S. Chae. (2010, Aug). Local directional pattern (LDP)-A robust image descriptor for object recognition. In Advanced Video and Signal Based Surveillance (AVSS), 2010 Seventh IEEE International Conference on, 482-487.
  15. S. I. Lee & C. D. Yoo. (2008). Robust video fingerprinting for content-based video identification. IEEE Transactions on Circuits and Systems for Video Technology, 18(7), 983-988. https://doi.org/10.1109/TCSVT.2008.920739
  16. J. Song, Y. Yang, Z. Huang, H. Shen & J. Luo. (2013). Effective multiple feature hashing for largescale near-duplicate video retrieval. IEEE Transactions on Multimedia, 15(8), 1997-2008. https://doi.org/10.1109/TMM.2013.2271746
  17. X. Lv & Z. J. Wang. (2013). Compressed binary image hashes based on semisupervised spectral embedding. IEEE Transactions on Information Forensics and Security, 8(11), 1838-1849. https://doi.org/10.1109/TIFS.2013.2281219
  18. S. C. Hwang. (2014). Development of Video Watermark System for Low-specification System as Android Platforms. Journal of The Korea Society of Computer and Information, 19(7), 141-149. https://doi.org/10.9708/jksci.2014.19.7.141
  19. M. Li & V. Monga. (2014). Twofold video hashing with automatic synchronization. 2014 IEEE International Conference on Image Processing (ICIP), 5362-5366.
  20. ITU. (2011). Recommendation ITU-R BT.601-7, Studio encoding parameters of digital television for standard 4: 3 and wide-screen 16: 9 aspect ratios.
  21. Y. Benjamini, & Y. Hochberg. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 57(1), 289-300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x