d391c1fd-ec30-4af4-a14a-310f5167b0cc20220121063950180naun:naunmdt@crossref.orgMDT DepositInternational Journal of Circuits, Systems and Signal Processing1998-446410.46300/9106http://www.naun.org/cms.action?id=30291720221720221610.46300/9106.2022.16https://npublications.com/journals/circuitssystemssignal/2022.phpZhihongHeSchool of Digital Engineering, Chongqing College of Architecture and Technology, Chongqing 401331, ChinaWenjieJiaSchool of Digital Engineering, Chongqing College of Architecture and Technology, Chongqing 401331, ChinaErhuaSunSchool of Digital Engineering, Chongqing College of Architecture and Technology, Chongqing 401331, ChinaHuilongSunSchool of Digital Engineering, Chongqing College of Architecture and Technology, Chongqing 401331, ChinaThe existing optimization methods have the problem of image edge blur, which leads to a high degree of shadow residue. In order to address this problem, reduce the shadow residual degree, this paper designs a 3D video image processing effect optimization method supported by virtual reality technology. Coding was used to eliminate redundant data in video and eliminate image noise using median filtering. The virtual reality technology detects the image edge and determines the motion offset between the image frames. According to the motion parameters of the camera carrier obtained from the motion estimation, the feature point matching algorithm constructs the video image motion model, and uses the camera calibration technology to set the processing effect optimization mode. It is optimized by perspective projection transformation. Experimental results: the average shadow residual degree of the optimization method and the two existing optimization methods are 3.108%, 6.167% and 6.396% respectively, which proves that the optimization method combined with virtual reality technology has higher practical application value.1122022112202238539047https://npublications.com/journals/circuitssystemssignal/2022/a942005-047(2022).pdf10.46300/9106.2022.16.47https://npublications.com/journals/circuitssystemssignal/2022/a942005-047(2022).pdf10.1109/access.2020.3023648F. Zhang and F. Wang, “Exercise fatigue detection algorithm based on video image information extraction,” IEEE Access, vol. 8, no. 2, pp. 199696-199709, 2020. 10.1109/tip.2018.2878505J. Dai, P. P. Zhang, D. Wang, H. C. Lu and H. Y. Wang, “Video person re-identification by temporal residual learning”, IEEE Transactions on Image Processing, vol. 28, no. 3, pp. 1366-1377, 2019.. 10.1007/s10694-019-00899-5H. Wang, Y. Zhang and X. 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