Image Compression Algorithm Based on Region of Interest Extraction for Unmanned Aerial Vehicles Communication

Abstract

Unmanned aerial vehicles (UAVs) are widely used but face challenges of limited storage and bandwidth. In this research, we propose an image compression algorithm tailored for UAV communication, termed region of interest extraction for UAV communication (ROIE-UC). First, image pixels are clustered into super pixel blocks using the simple linear iterative clustering (SLIC). Second, these super pixels are grouped into regions of interest (ROI) using the density-based spatial clustering of applications with noise (DBSCAN). The image is then segmented into ROI and non-ROI areas based on these clusters. Lossless compression is applied to the ROI, while lossy compression with a high ratio is used for non-ROI regions. At the receiving end, the image is decompressed and reconstructed. Experiments show ROIE-UC gets a peak signal-to-noise ratio (PSNR) of 46.37 dB and an feature similarity index (FSIM) of 99.99% for ROI. It outperforms JPEG in PSNR (up to 28.52% improvement), FSIM (0.15% improvement), and compression ratio. When PSNR and FSIM are similar, its max compression ratio is 5.89 times that of JPEG. It also has up to 51.49% higher PSNR than other methods. ROIE-UC is an effective solution for UAV image processing and data compression.