Advanced Learning-Based Coding Tools for ECM: Intra Prediction and In-Loop Filtering

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.

Recommended citation: Zhao, Y., Fu, J., Li, Z., Wang, Q., Huang, Z., Zhang, J., Jia, C. and Ma, S., 2025, May. Advanced Learning-Based Coding Tools for ECM: Intra Prediction and In-Loop Filtering. In Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE.
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