  This paper presents a video coding scheme that combines traditional
optimization methods with deep learning methods based on the Enhanced
Compression Model (ECM). In this paper, the traditional optimization methods
adaptively adjust the quantization parameter (QP). The key frame QP offset is
set according to the video content characteristics, and the coding tree unit
(CTU) level QP of all frames is also adjusted according to the spatial-temporal
perception information. Block importance mapping technology (BIM) is also
introduced, which adjusts the QP according to the block importance. Meanwhile,
the deep learning methods propose a convolutional neural network-based loop
filter (CNNLF), which is turned on/off based on the rate-distortion
optimization at the CTU and frame level. Besides, intra-prediction using neural
networks (NN-intra) is proposed to further improve compression quality, where 8
neural networks are used for predicting blocks of different sizes. The
experimental results show that compared with ECM-3.0, the proposed traditional
methods and adding deep learning methods improve the PSNR by 0.54 dB and 1 dB
at 0.05Mbps, respectively; 0.38 dB and 0.71dB at 0.5 Mbps, respectively, which
proves the superiority of our method.
