FrameCorr: Adaptive, Autoencoder-based Neural Compression for Video Reconstruction in Resource and Timing Constrained Network Settings
This addresses video transmission challenges for IoT applications, but it appears incremental as it builds on existing autoencoder-based methods.
The paper tackles the problem of video compression for IoT devices under bandwidth and timing constraints by introducing FrameCorr, which uses deep learning to predict missing frame segments from previously received data, enabling reconstruction from partial data.
Despite the growing adoption of video processing via Internet of Things (IoT) devices due to their cost-effectiveness, transmitting captured data to nearby servers poses challenges due to varying timing constraints and scarcity of network bandwidth. Existing video compression methods face difficulties in recovering compressed data when incomplete data is provided. Here, we introduce FrameCorr, a deep-learning based solution that utilizes previously received data to predict the missing segments of a frame, enabling the reconstruction of a frame from partially received data.