FrameCorr：面向资源与时序受限网络环境中视频重建的、基于自编码器的自适应神经压缩
FrameCorr: Adaptive, Autoencoder-based Neural Compression for Video Reconstruction in Resource and Timing Constrained Network Settings
AI总结：
本文提出 FrameCorr，一种基于自编码器和深度学习的自适应视频压缩方法，可在带宽与时序受限的 IoT 网络中利用已接收数据补全缺失片段并重建视频帧。
摘要：
尽管通过物联网（IoT）设备进行视频处理因具有成本效益而日益普及，但由于时序约束不断变化且网络带宽稀缺，将采集到的数据传输至附近服务器仍面临挑战。现有视频压缩方法在只提供不完整数据时，难以恢复压缩数据。为此，我们提出 FrameCorr，这是一种基于深度学习的解决方案，利用先前接收的数据预测帧的缺失片段，从而能够根据部分接收的数据重建该帧。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
