Research on Intelligent Estimation Model of BER for High-Speed Image Transmission Based on LVDS Interface
The high-speed image signal of LVDS interface is easy to be interfered by the outside world in the process of transmission, which results in packet loss and distortion of high-speed image communication, and the output error is high. Therefore, the lossless coding of high-speed image signal is needed. Intelligent estimation of bit error rate (BER) for high-speed image transmission is needed. The intelligent estimation model of high-speed image transmission bit error rate based on LVDS interface is proposed. The network structure model of high-speed image signal transmission is constructed to estimate the error code distortion of image transmission and the key frame feature extraction method is used to estimate the error rate of image transmission. The intelligent estimation of bit error rate (BER) of high-speed image transmission is realized in LVDS interface. The simulation results show that the proposed method has low bit error rate (BER) for high-speed image transmission and achieves lossless transmission of images.
KeywordsLVDS interface High speed image Transmission Bit error rate Intelligent estimation
- 1.Han, D., Chen, X., Lei, Y., et al.: Real-time data analysis system based on Spark Streaming and its application. J. Comput. Appl. 37(5), 1263–1269 (2017)Google Scholar
- 2.Sun, D.W., Zhang, G.Y., Zheng, W.M.: Big data stream computing, technologies and instances. J. Software 25(4), 839–862 (2014)Google Scholar
- 9.Zhu, Y., Zhu, X., Wang, J.: Time series motif discovery algorithm based on subsequence full join and maximum clique. J. Comput. Appl. 39(2), 414–420 (2019)Google Scholar
- 13.Zhang, R., Zhao, F.: Foggy image enhancement algorithm based on bidirectional diffusion and shock filtering. Comput. Eng. 44(10), 221–227 (2018)Google Scholar
- 14.Liu, D., Zhou, D., Nie, R., Hou, R.: Multi-focus image fusion based on phase congruency motivate pulse coupled neural network-based in NSCT domain. J. Comput. Appl. 38(10), 3006–3012 (2018)Google Scholar