Advanced Learning-Based Coding Tools for ECM: Intra Prediction and In-Loop Filtering
Yanchen Zhao, Jiaye Fu, Zhaoyu Li, Qizhe Wang, Zhimeng Huang, Jiaqi Zhang, Chuanmin Jia, Siwei Ma · 2025
Neural Network (NN)-based video coding technologies have emerged as a promising alternative to traditional methods, demonstrating significant advantages amidst the rapid advancements in video coding technology. This paper presents a hybrid video coding method based on the Enhanced Compression Model (ECM) developed by the Joint Video Exploration Team (JVET). We integrate two NN-based coding tools into the framework. Specifically, the proposed NN-based Intra Prediction (NNIP) method effectively models the nonlinear relationship between neighboring contextual information and the block to be predicted. The NN-based In-Loop Filtering (NNILF) method adaptively filters the luminance and chrominance components across various quality levels. Experimental results show that the NNIP and NNILF methods achieve 0.56% and 4.14% BD-rate savings for YCbCr components under the All Intra (AI) configuration compared to ECM-14.0. Under the Random Access (RA) configuration, the proposed method can achieve a 2.41% BD-rate saving for YCbCr components.