A Convolutional Neural Network Image Compression Algorithm for UAVs

Abstract

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.