MLISP: Machine-Learning-based ISP Decision Scheme for VVC Encoders - Araújo, Larissa; Duarte, Adson; Zatt, Bruno; Correa, Guilherme; Palomino, Daniel (Journal of the Brazilian Computer Society, 2025)
The Versatile Video Coding (VVC) standard achieves high compression rates by introducing new encoding tools, such as the Intra Subpartition Prediction (ISP). However, the ISP increases the computational effort necessary to perform the mode decision in the intra-prediction step. In this paper, we propose the MLISP, a machine learning-based ISP decision scheme for VVC encoders where two solutions are adopted to accelerate the intra-mode decision process for the ISP tool. The first solution, named ISP Skip Decision, utilizes a Decision Tree trained with image features that predicts whether the evaluation of the ISP tool is necessary, resulting in an average time saving of 8.53% with only 0.22% of coding efficiency loss. The second solution called ISP Mode Decision, uses a Decision Tree trained with encoding features to predict the optimal class of intra modes between Planar/DC and Angular to be evaluated with the ISP tool, obtaining an average time saving of 7.01% with only 0.19% of coding efficiency loss. By combining these solutions, MLISP achieves an average time saving of 10.97% with only 0.32% loss in coding efficiency, demonstrating its effectiveness in reducing encoding time with minimal impact on compression performance. Compared with related works, MLISP achieves competitive results and introduces a novel approach for optimizing the ISP decision.