FrameCorr: Adaptive, Autoencoder-based Neural Compression for Video Reconstruction in Resource and Timing Constrained Network Settings
<|reference_start|>FrameCorr: Adaptive, Autoencoder-based Neural Compression for Video Reconstruction in Resource and Timing Constrained Network Settings: 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.<|reference_end|>
@article{li2024framecorr:,
    title={FrameCorr: Adaptive, Autoencoder-based Neural Compression for Video
  Reconstruction in Resource and Timing Constrained Network Settings},
    author={John Li, Shehab Sarar Ahmed, Deepak Nair},
    journal={arXiv preprint arXiv:2409.02453},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02453},
    primaryClass={eess.IV cs.CV cs.ET cs.MM}
}