Integrating Adaptive Sampling for Optimal Learned Video Compression

We propose a novel adaptive prediction network that dynamically determines the optimal sampling factor and Lagrangian multiplier for encoding each frame, guided by sequential information. By exploiting spatio-temporal redundancy through adaptive sampling, our method reduces bitrate consumption while preserving the reconstruction quality with adjusted rate-distortion coefficients. Experimental results demonstrate significant performance gains over representative learned video compression models across various datasets, with reduced encoding and decoding latency.