Compression Ratio Learning and Semantic Communications for Video Imaging

Abstract

It is crucial to improve data acquisition and transmission efficiency for mobile robots with limited power, memory, and bandwidth resources. For efficient data acquisition, a novel video compressed-sensing system with spatially-variant compression ratios is designed, which offers high imaging quality with low sampling rates; To improve data transmission efficiency, semantic communication is leveraged to reduce bandwidth requirement, which provides high image recovery quality with low transmission rates. In particular, we focus on the trade-off between rate and quality. To address the challenge, we use neural networks to decide the optimal rate allocation policy for given quality requirements. Due to the non-differentiable issue of rate, we train the networks by policy-gradient-based reinforcement learning. Numerical results show the superiority of the proposed methods over the existing baselines.