Aprendizado de Máquina Aplicado à Redução do Custo Computacional do Test Zone Search e da Estimação de Movimento Affine do Codificador VVC - Viana, Ramiro Gomes da Silva; Agostini, Luciano Volcan (Anais Estendidos do Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia), 2025)
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 requirements of efficient video delivery and consumption, Versatile Video Coding (VVC) emerges as the state-of-the-art. This dissertation focuses on two key encoding tools within VVC Inter-Frame prediction: Test Zone Search (TZS) and Affine Motion Estimation (AME). A fast TZS and AME approach using Machine Learning is presented, specifically employing Decision Trees. The proposed approach achieved an average reduction of over 20% in total VVC encoding time while maintaining less than a 1% impact on BD-BR coding efficiency.