The technology of unmanned aerial vehicles (UAVs) is extensively utilized across various domains due to its exceptional mobility, cost-effectiveness, rapid deployment, and flexible configuration. However, its limited storage space and the contradiction between the large amount of image data returned and the limited bandwidth in emergency scenarios necessitate the development of a suitable UAVs image compression technology. In this research, we propose an image splicing compression algorithm based on the extended Kalman filter for UAV communication (SEKF-UC). Firstly, we introduce the concept of image splicing to enhance the compression ratio while ensuring image quality, leveraging the characteristics of deep learning-based compression, which are relatively unaffected by the size of the compressed data. Secondly, to address the issue of slow processing speed of deep neural networks (DNN) caused by the substantial volume of data involved in image splicing, we incorporate an extended Kalman filter module to expedite the process. Simulation experiments show that our proposed algorithm outperforms existing methods in multiple ways, achieving a significant compression ratio improvement, from 2:1 to 25:1, with a marginal reduction of 6.5% in structural similarity (SSIM) compared to the non-spliced approach. Moreover, for deep neural networks (DNN), our method incorporating the extended Kalman filter module achieves the same error level with only 30 iterations—a significant reduction compared to the traditional BP algorithm’s requirement of over 4000 iterations—while improving the average network operation speed by an impressive 89.76%. Additionally, our algorithm excels in image quality, with peak signal-to-noise ratio (PSNR) improving by 92.7% and SSIM by 42.1%, at most, compared to other algorithms. These results establish our algorithm as a highly efficient and effective solution, suitable for various image processing and data compression applications.


    Access

    Download


    Export, share and cite



    Title :

    Image Splicing Compression Algorithm Based on the Extended Kalman Filter for Unmanned Aerial Vehicles Communication


    Contributors:
    Yanxia Liang (author) / Meng Zhao (author) / Xin Liu (author) / Jing Jiang (author) / Guangyue Lu (author) / Tong Jia (author)


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Vision-based terrain referenced navigation of aerial vehicles using an adaptive extended Kalman filter

    Mok, Sung-Hoon / Kim, Youngjoo / Bang, Hyochoong | SAGE Publications | 2018



    Splicing type unmanned aerial vehicle

    KONG WEILIANG / CHEN QUANLONG / CHAI JIAO et al. | European Patent Office | 2023

    Free access

    Vision-only Navigation and Control of Unmanned Aerial Vehicles Using the Sigma-Point Kalman Filter

    Bai, H. / Wan, E. / Song, X. et al. | British Library Conference Proceedings | 2007


    Extended Kalman Filter Based Magnetic Guidance for Intelligent Vehicles

    Xu, H.G. / Wang, C.X. / Yang, R.Q. et al. | IEEE | 2006