In the task of power line inspection, Unmanned Aerial Vehicles (UAVs) are frequently used for capturing images. With the rapid advancement of sensor technology, the spatial, radiometric, and spectral resolutions of UAV images are constantly improving, leading to an increased storage requirement for individual images. Given that UAVs usually operate with limited computational resources, transmission capability and storage space, there are significant challenges in image compression, storage and transmission. This underscores the importance of a high-performance image compression technique. To solve the above problem, we unveil a compression strategy for images that have been acquired through learning utilizing discrete Gaussian mixture-based probability distributions to increase the efficiency of image compression and the fidelity of reconstruction. In addition, to speed up decoding, we employ a parallel context model, which facilitates decoding in a highly parallel manner. Experimental evidence indicates that our approach attains performance that is at the forefront of the field while significantly expediting the decoding process (speeding up the decoding process by more than 49.78%) in our experiments, outpacing traditional coding standards and existing learned compression approaches by 5.75$$dB and 1.23$$dB in PSNR.

在电力线路巡检任务中，经常使用无人机（UAV）来捕获图像。随着传感器技术的快速进步，无人机图像的空间、辐射和光谱分辨率不断提高，导致单个图像的存储需求不断增加。由于无人机通常在计算资源、传输能力和存储空间有限的情况下运行，因此图像压缩、存储和传输面临着巨大的挑战。这强调了高性能图像压缩技术的重要性。为了解决上述问题，我们提出了一种利用基于离散高斯混合的概率分布学习获得的图像的压缩策略，以提高图像压缩的效率和重建的保真度。此外，为了加快解码速度，我们采用了并行上下文模型，这有助于以高度并行的方式进行解码。实验证据表明，我们的方法获得了该领域最前沿的性能，同时在我们的实验中显着加快了解码过程（将解码过程加快了 49.78% 以上），比传统编码标准和现有的学习压缩方法快了 5.75$$分贝和1.23$$PSNR 中的 dB。