DDVC: Deep Distributed Video Coding Using Quality Enhancement Network

Distributed video coding (DVC) transfers the complex process of the encoder to the decoder, which is suitable for video applications with limited encoding resources. Deep learning has shown impressive performance in video coding tasks in learning nonlinear compact representations of input frames and reconstructing video frame details. It is worth exploring whether deep learning implementation of the DVC paradigm is feasible and whether performance gains can be obtained. This paper proposes a deep DVC scheme (DDVC) using a quality enhancement network (QEN), which maps pixels to a more compressible latent space via an autoencoder resulting in a compact representation of Wyner-Ziv (WZ) frames. Moreover, considering the spatio-temporal correlation between the WZ frame and the Key frame, the QEN on the decoder side, using CNN and LSTM iteratively extracts common information between the WZ frame and the Key frame, which could further finetune the WZ frame reconstruction. We evaluated DDVC in limited encoding resources application scenarios with 19 related video sequences. Results on the video sequences with different motion intensity levels show that DDVC significantly outperforms existing schemes in reconstruction quality with the same compression ratio. We open-sourced the implementation at GitHub<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.