As the demand for video transmission surges in remote work, education, and streaming services, the need for continuous advancements in video encoding technologies becomes increasingly evident. Adapting to the evolving demands of efficient video delivery and consumption requires continuous advancement and refinement of video encoding standards. In this scenario, the Versatile Video Coding (VVC) stands out as a leading example, representing the current state-of-the-art in video coding. This work presents a Machine Learning (ML) approach to accelerate one of the novel tools within the VVC: the Affine Motion Estimation (AME). AME is part of the VVC Inter-Frame Prediction, and it is one of the most computationally intensive tools within VVC. The hardware-friendly Decision Tree ML method was explored in this work to accelerate the AME. A total of 12 Decision Trees were trained to determine whether to skip or not to skip the AME for each block size supported by AME in VVC. The proposed approach achieved an average reduction of 5.54% in the total VVC encoding time and an average reduction of 63.20% in the AME execution time, resulting in an average BD-BR efficiency loss of only 0.50%. These results demonstrate that the strategy of using Machine Learning to reduce the computational cost of VVC has the potential to continue yielding significant results in future solutions.
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