DOI QR코드

DOI QR Code

Reconstructed Iimage Quality Improvement of Distributed Compressive Video Sensing Using Temporal Correlation

시간 상관관계를 이용한 분산 압축 비디오 센싱 기법의 복원 화질 개선

  • 류중선 (한밭대학교 멀티미디어공학과) ;
  • 김진수 (한밭대학교 정보통신공학과)
  • Received : 2017.02.25
  • Accepted : 2017.04.14
  • Published : 2017.04.30

Abstract

For The Purpose of Pursuing the Simplest Sampling, a Motion Compensated Block Compressed Sensing with Smoothed Projected Landweber (MC-BCS-SPL) has been Studied for an Effective Scheme of Distributed Compressive Video Sensing with all Compressed Sensing (CS) Frames. However, Conventional MC-BCS-SPL Scheme is Very Simple and so it Does not Provide Good Visual Qualities in Reconstructed Wyner-Ziv (WZ) Frames. In this Paper, the Conventional Scheme of MC-BCS-SPL is Modified to Provide Better Visual Qualities in WZ Frames. That is, the Proposed Agorithm is Designed in such a way that the Reference Frame may be Adaptively Selected Based on the Temporal Correlation Between Successive Frames. Several Experimental Results show that the Proposed Algorithm Provides Better Visual Qualities than Conventional Algorithm.

가장 간단한 샘플링을 위한 목적으로 SPL (Smoothed Projected Landweber)기법 기반의 움직임 보상 블록 압축센싱 기법이 모든 센싱 프레임들에 대해 분산 압축 비디오 센싱 기술이 적용되는 효과적인 방안으로 연구되어 오고 있다. 그러나 기존의 움직임 보상 블록기반의 압축센싱 기법은 매우 간단하여 복원된 위너-지브 프레임에서 우수한 화질을 제공하지 못하는 한계점이 있다. 본 논문에서는 기존의 움직임 보상 블록기반의 압축센싱 기법을 이용한 위너-지브 프레임에서 우수한 화질을 제공될 수 있도록 알고리즘을 변형한다. 즉, 제안된 알고리즘은 참조 프레임이 연속적인 프레임들에 있어 시간적 상관관계에 기초해서 적응적으로 선택되도록 하는 방법으로 설계된다. 다양한 실험 결과를 통하여 제안한 알고리즘은 기존의 알고리즘에 비해 우수한 화질을 제공할 수 있음을 확인한다.

Keywords

References

  1. SlepianD. and Wolf J., "Noiseless Coding of Correlated Information Sources," in Proceedings of IEEE Trans. on Information Theory 19, pp. 471-480, July 1973. https://doi.org/10.1109/TIT.1973.1055037
  2. Girod B., Aaron A., Rane S., and Rebollo-Monedero D., "Distributed Video Coding," in Proceedings of IEEE Special Issue On Advance In Video Coding And Delivery, Vol. 93, pp. 71-83, June 2005.
  3. Do T., Chen Y., Nguyen D. T., Nguyen N., Gan L., and Tran T. D., "Distributed Compressed Video Sensing," in Proceedings of the International Conference on Image Processing, Cairoa, Egypt, pp. 1393-1396, November 2009.
  4. Donoho D. L., "Compressed Sensing," IEEE Transactions on Information Theory, Vol. 52, No.4, pp. 1289-1306, Apr. 2006. https://doi.org/10.1109/TIT.2006.871582
  5. Jeon B., "Compressed Sensing and Image Processing Application," Proceedings of The Magazine of the The Institute of Electronics and Information Engineers, Vol. 41, No.6, pp. 27-38, June. 2014.
  6. Mun S. and Fowler J. E., "Block Compressed Sensing of Images Using Directional Transforms," in Proceedings of IEEE International Conference on Image Processing, USA, pp. 3021-3024, 2009.
  7. Mun S. and Flower J. E., "Residual Reconstruction for Block-based Compressed Sensing of Video," in Proceedings of Data Compression Conference, pp. 183-192, March 2011.
  8. Nguyen Q. H., Dinh K. Q., Nguyen V. A., Trinh C. V., Park Y. H., Jeon B. W.. "A Skip-mode Coding for Distributed Compressive Video Sensing," Journal of Broadcast Engineering, Vol. 19, No.2. pp 257-267, March. 2014. https://doi.org/10.5909/JBE.2014.19.2.257
  9. Gan L., "Block Compressed Sensing of Natural Images," Proceedings of the International Conference on Digital Signal Processing, Cardiff, UK, pp. 403-406, July. 2007.
  10. Fowler J. E., Mun S., and Tramel E. W., "Multiscale Block Compressed Sensing with Smoothed Projected Landweber Reconstruction," in Proceedings of 19th European Signal Processing Conference, Aug 2011, pp. 564-568.
  11. Park Y., Shin H., Jeon B., "Convergence Complexity Reduction for Block-Based Compressive Sensing Reconstruction," Journal of The Korean Society of Broadcast Engineering, Vol. 19, No.2, pp. 240-249, Mar. 2014. https://doi.org/10.5909/JBE.2014.19.2.240
  12. Ryu J. S. and Kim J. S. "Performance Comparison of BCS-SPL Techniques Against a Variety of Restoring Block Sizes," Journal of the Korea Industrial Information System Society, Vol. 21, No.1, pp. 21-28, 2016.
  13. Kim J. and Lee B., "Wave Information Retrieval Algorithm Based on Iterative Refinement," Journal of the Korea Industrial Information System Society, Vol. 21, No.1, pp.7-15, 2016.
  14. Kwon S. and Lee D., "Recognition Method of Multiple Objects for Virtual Touch Using Depth Information," Journal of the Korea Industrial Information System Society, Vo1. 21, No.1, pp.27-34, 2016. https://doi.org/10.9723/jksiis.2016.21.1.027
  15. Kim J., "The Decoding Approaches of Genetic Algorithm for Job Shop Scheduling Problem," The Journal of Information Systems, Vol. 25, No.2, 2016, pp.105-117
  16. Han H., Chung N., Koo C., "Utilizing Smart Technologies to Enhance Tourists' Experiences at the Exhibition : A Case of Near Field Communication," The Journal of Internet Electronic Commerce Research, Vol. 16, No.5, 2016, pp.1-19. https://doi.org/10.1007/s10660-015-9209-0

Cited by

  1. 효과적인 MC-BCS-SPL 알고리즘과 예측 구조 방식에 따른 성능 비교 vol.21, pp.7, 2017, https://doi.org/10.6109/jkiice.2017.21.7.1355
  2. 신뢰성 예측을 이용한 분산 압축 비디오 센싱의 성능 개선 vol.23, pp.6, 2017, https://doi.org/10.9723/jksiis.2018.23.6.047
  3. 360 VR 기반 파노라마 영상 구성을 위한 칼라 및 밝기 보상 알고리즘 vol.24, pp.1, 2017, https://doi.org/10.5909/jbe.2019.24.1.3
  4. 인쇄된 컬러 QR코드의 합성곱 신경망 알고리즘에 의한 진위 판정 시스템 vol.25, pp.3, 2020, https://doi.org/10.9723/jksiis.2020.25.3.021