Fast Anti-noise Compression Storage Algorithm for Big Data Video Images

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 302)


When calculating the traditional image compression storage algorithm, the key frames of the video image are mainly extracted by the video image feature. In the process of video image acquisition, the influence of factors such as light is detected, and the image features are changed, resulting in a large storage problem. A new anti-noise compression storage algorithm for big data video images is proposed. First, the collected big data video images are divided. The average value of the gray of the image sub-region is obtained, and then the compression process and the stored procedure are given. The actual working effect of the algorithm is verified by comparison with the traditional algorithm. The experimental results show that the improved algorithm is well stored and the error is small. The fast anti-noise compression storage method for the big data video images studied in this paper has a good storage effect, and its application range is wider and more worthy of promotion.


Big data Video image Fast compression Anti-noise compression Storage algorithm 


  1. 1.
    Bello-Orgaz, G., Jung, J.J., Camacho, D.: Social big data: recent achievements and new challenges. Inf. Fusion 28, 45–59 (2016)CrossRefGoogle Scholar
  2. 2.
    Sowmya, R., Suneetha, K.R.: Data mining with big data. In: International Conference on Intelligent Systems and Control, pp. 246–250. IEEE (2017)Google Scholar
  3. 3.
    Aishwarya, K.M., Ramesh, R., Sobarad, P.M, et al.: Lossy image compression using SVD coding algorithm. In: International Conference on Wireless Communications, Signal Processing and Networking, pp. 1384–1389. IEEE (2016)Google Scholar
  4. 4.
    Masyarif, S., Kurniawan, A.: Harmony search algorithm with dynamic pitch adjustment rate and fret width for image compression. In: Multimedia and Broadcasting, pp. 66–72. IEEE (2016)Google Scholar
  5. 5.
    Bharathi, M., Janani, T.: Fractal image compression using quantum search algorithm. J. Comput. Theor. Nanosci. 14(9), 4580–4585 (2017)CrossRefGoogle Scholar
  6. 6.
    Gu, Y., Jiang, H., Xie, X., et al.: An image compression algorithm for wireless endoscopy and its ASIC implementation. In: Biomedical Circuits and Systems Conference, pp. 103–106. IEEE (2017)Google Scholar
  7. 7.
    Kamargaonkar, C., Sharma, M.: Hybrid medical image compression method using SPIHT algorithm and Haar wavelet transform. In: International Conference on Electrical, Electronics, and Optimization Techniques, pp. 897–900. IEEE (2016)Google Scholar
  8. 8.
    Zheng, F., Zhang, C., Zhang, X., et al.: A fast anti-noise fuzzy C-means algorithm for image segmentation. In: IEEE International Conference on Image Processing, pp. 2728–2732. IEEE (2014)Google Scholar
  9. 9.
    Krueger, J., Nicolai, M.: Sound generator for an anti-noise system for influencing exhaust noises and/or intake noises of a motor vehicle: US9374632 (2016)Google Scholar
  10. 10.
    He, G., Wei, Y.: An anti-noise fusion method for the infrared and the visible image based upon sparse representation. In: International Conference on Machine Vision and Information Technology, pp. 12–17. IEEE (2017)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.South China Normal UniversityGuangzhouChina

Personalised recommendations