DCMFF-Net: A Low-Complexity Intra-Frame Encoding Method with Double Convolution and Multi-Scale Feature Fusion

H. 265/HEVC still dominates the video encoding application market with its mature industrial ecosystem and excellent hardware support. However, its high computational complexity remains a major barrier to its wider application. To tackle this problem, we introduce an efficient intra-encoding approach that leverages double convolution, multi-scale feature fusion. Firstly, a three-branch architecture aims to capture multi-scale information from the source image, and then use non-overlapping and overlapping parallel convolution structures in each branch to achieve feature fusion for each branch. Secondly, combined with an attention mechanism, the output features of multiple branches are fused to highlight important features, reduce detail loss, and effectively balance encoding quality and complexity. Finally, by combining multi-scale feature fusion and double convolutional feature fusion, a feature hybrid network is formed to accurately predict whether the coding unit (CU) should be divided, and achieve fast encoding. Experimental results on multiple datasets demonstrate that against the traditional HM16.5 benchmark, our method reduces the average encoding time by 63.52%, with a marginal BD-BR rise of 1.95% and a BD-PSNR drop of 0.09 dB, demonstrating its superiority.