Adaptive Compressed Sensing for Real-Time Video Compression, Transmission, and Reconstruction

Yaping Zhao, Qunsong Zeng, Edmund Y. Lam

Abstract: The real-time transmission of videos with both high resolution and high frame rate is challenging, due to the limited storage space and significant communication overhead. To meet the real-time requirement, these issues are usually tackled by video quality reduction, which compromises the user experience. While previous methods, such as video compressed sensing, have attempted to address these issues, they often employ a fixed compression rate without considering the varying channel gain and do not adequately address the real-time transmission requirements. To mitigate these shortcomings, we propose an adaptive compressed sensing framework that optimizes the compression rate based on the channel state. This approach equivalently optimizes the video quality while ensuring real-time transmission by reducing communication overhead and thus latency. The feasibility and performance of our method are validated and discussed through extensive experiments on both classic and custom datasets.