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

A Video Coding Method Based on Neural Network for CLIC2024