Temporal Down-sampling based Video Coding with Frame-Recurrent Enhancement
Keren He, Chen Ming Fu, Chi Do-Kim Pham, Lu Zhang, Jinjia Zhou · 2023
In many digital systems, the transmission bandwidth, as well as storage capacity, are usually very limited. This introduces challenges for both video transmission and video storage. To seek lower bit rates and further obtain high-quality up-sampled videos, this paper proposes a temporal down-sampling based video coding system and a frame-recurrent enhancement based video upsampling strategy. The structure of our proposed method is shown in Fig. 1. Unlike the existing work [1], instead of downsampling all video frames, only the intermediate frames are downsampled and two frames remain with high quality on the video coding system. Then, these two high-quality frames are used to iteratively enhance the quality of the low-bitrate low-quality frames through a deep-learned enhancement network. Compared to the latest video coding standard Versatile Video Coding (VVC), our work can obtain a BD-rate reduction from $39.261 {\%} \sim 85. 455$ % in All-Intra and Low-Delay-P configurations on the downsampled frames. A temporal down-sampling based video coding framework (TDS) is proposed. It can be combined with all the existing coding standards including HEVC/H.265 and VVC/H.266. A method of super-resolution with frame recurrent image enhancement (SRFR) is applied to up-sampling the frames by the neighboring high resolution frame. The temporal information from high resolution frames can be fully used to improve the video quality through frame recurrent.