Suppression ofn the video stream frames processed by unmanned systems using FPV control

封面

如何引用文章

全文:

详细

Using packet networks for first-person control of unmanned systems arises a problem of large transmitted data volumes. The largest volume of traffic during first-person control is presented by video stream frames. So, to improve the efficiency of the communication network between unmanned systems and external pilot station, it is necessary to compress video stream frames. A high compression degree can be provided by using variational autoencoders. One of the problems of using variational autoencoders for frame compression is the occurrence of specific artifacts in frames. This article proposes methods for suppressing the occurrence of artifacts when restoring frames from the latent space by a neural network decoder, as well as an empirical scale for assessing autoencoder artifacts. The approach proposed encompasses preparing pixel data of a video stream frame for encoding and further reconstruction after decoding. It is experimentally shown that one of the proposed methods allows eliminating the absolute majority of artifacts without introducing significant distortions into the reconstructed frames.

作者简介

А. Berezkin

Bonch-Bruevich Saint Petersburg State University of Telecommunications

编辑信件的主要联系方式.
Email: berezkin.aa@sut.ru

Associate Professor of Program Engineering and Computer Science Department, PhD in Technical Science

俄罗斯联邦, Saint Petersburg

A. Chenskiy

Bonch-Bruevich Saint Petersburg State University of Telecommunications

Email: chenskii.aa@sut.ru

Master’s Degree Student of Program Engineering and Computer Science Department

俄罗斯联邦, Saint Petersburg

R. Kirichek

Bonch-Bruevich Saint Petersburg State University of Telecommunications

Email: kirichek@sut.ru

Rector, Professor of Program Engineering and Computer Science Department, Doctor of Technical Science

俄罗斯联邦, Saint Petersburg

参考

  1. Berezkin A.A. et al. Research of latent video stream compression methods for FPV control of uavs. Elektrosvjaz’, 2024, no. 6, pp. 26–36. (In Russ.)
  2. Berezkin A.A. et al. Research of latent space quantization methods of variational autoencoder for FPV video stream frames. Part I. Elektrosvjaz’, 2024, no. 6, pp. 10–16. (In Russ.)
  3. Project of the strategy for the development of the telecommunications industry of the Russian Federation for the period up to 2035 [adopted by the Government of Russian Federation on November 24, 2023]. URL: https://digital.gov.ru/ru/documents/9120/ (accessed: 27.07.2024). (In Russ.)
  4. ITU-Т Recommendation H.264. Improved Image Encoding for General Audiovisual Services. Geneva, 2008, 342 p. (In Russ.)
  5. ITU-T Recommendation H.265 (V9). High Efficiency Video Coding. Geneva, 2023, 718 p.
  6. ISO/IEC 10918-1:1994. Information Technology – Digital Compression and Coding of Continuous-Tone Still Images: Requirements and Guidelines. URL: https://www.iso.org/ru/standard/ 18902.html (accessed: 27.07.2024).
  7. Barman N., Martini M.G. An evaluation of the next-generation image coding standard AVIF. 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), 2020, pp. 1–4.
  8. Ginesu G., Pintus M., Giusto D.D. Objective assessment of the WebP image coding algorithm. Signal Processing: Image Communication, 2012, vol. 27, no. 8, pp. 867–874.
  9. Berezkin A.A. et al. Research of methods of quantization of latent space of variational autoencoder for FPV video stream frames. Part II. Elektrosvjaz’, 2024, no. 7, pp. 16–25. (In Russ.)
  10. Jiang J., Zhang K., Timofte R. Towards flexible blind JPEG artifacts removal. IEEE/CVF International Conference on Computer Vision, 2021, pp. 4997–5006.
  11. Saveljev V., Kim S. K., Kim J. Moire effect in displays: A tutorial. Optical Engineering, 2018, vol. 57, no. 3. URL: https://www.researchgate.net/publication/324074641_Moire_effect_in_displays_A_tutorial (accessed: 07.2024).
  12. Zaitsev M., Maclaren J., Herbst M. Motion artifacts in MRI: A complex problem with many partial solutions. Journal of Magnetic Resonance Imaging, 2015, vol. 42, no. 4, pp. 887–901.
  13. Castellanos N.P., Makarov V.A. Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis. Journal of Neuroscience Methods, 2006, vol. 158, no. 2, pp. 300–312.
  14. Wagenaar D.A., Potter S.M. Real-time multi-channel stimulus artifact suppression by local curve fitting. Journal of Neuroscience Methods, 2002, vol. 120, no. 2, pp. 113–120.
  15. Galteri L. et al. Deep generative adversarial compression artifact removal. IEEE International Conference on Computer Vision (ICCV), 2017, pp. 4826–4835.
  16. Cavigelli L., Hager P., Benini L. CAS-CNN: A deep convolutional neural network for image compression artifact suppression. 2017 International Joint Conference on Neural Networks (IJCNN), 2017, pp. 752–759.
  17. Yeh C.H. et al. Deep learning-based compressed image artifacts reduction based on multi-scale image fusion. Information Fusion, 2021, vol. 67, no. 4, pp. 195–207.
  18. Github. Stable diffusion: development repository. URL: https://github.com/pesser/stable-diffusion/tree/main (accessed: 28.07.2024).
  19. Github. Lossy Image compression with conditional diffusion models.URL: https://github.com/buggyyang/CDC_compression (accessed: 28.07.2024).

补充文件

附件文件
动作
1. JATS XML

版权所有 © Berezkin А.А., Chenskiy A.A., Kirichek R.V., 2025

Creative Commons License
此作品已接受知识共享署名-非商业性使用-禁止演绎 4.0国际许可协议的许可。