FrameCorr: Adaptive, Autoencoder-based Neural Compression for Video Reconstruction in Resource and Timing Constrained Network Settings

Video processing is becoming increasingly popular and cost-effective on IoT devices but faces challenges in transmitting data under varying timing constraints and network bandwidth. Existing compression methods struggle with incomplete data. We present FrameCorr, a deep learning framework that leverages prior-received video data to predict and reconstruct missing frame segments, enabling video reconstruction despite data loss.