Optimal Transport With A New Preprocessing For Deep-Learning Full Waveform inversion

The learning-based end-to-end video compression exhibits a fast development with continuous improvements. in previous works, a key frame in random-access scenarios is typically compressed by an image compressor, and the remaining frames are reconstructed by interpolations. But solely using image compression fails to leverage the temporal correlations, which is critical to achieve a substantial gain in video compression. To exploit temporal correlations among key frames, we introduce a learning-based end-to-end spatial-temporal adaptive (e2e-STA) compression solution to offer flexible options for the key frames. First of all, we design an extrapolation-based key frame compression scheme. Given a key frame, e2e-STA can switch between an image compressor and an extrapolative compressor. A key frame is thereby able to select the optimal solution adaptively according to the rate-distortion optimization criteria and the optimal selection is sent to the decoder. The proposed approach is optimized end-to-end with all the networks. The experimental results validate the effectiveness of the proposed mechanism. The proposed method outperforms existing learning-based video compression methods by a noticeable margin and provides promising performance compared to traditional benchmark video codecs in terms of PSNR and MS-SSIM.