Paralelização de Tarefas de Codificação de Vídeo VVC utilizando GPGPUs

The compression required in video-based applications imposes a significant computational workload. Moreover, computing systems are becoming increasingly heterogeneous, and GPUs have gained popularity. This Ph.D. Thesis proposes a GPU acceleration methodology that provides guidelines for efficiently leveraging heterogeneous CPU+GPU systems to accelerate video coding applications. Experimental results demonstrate that the solutions developed following the proposed methodology accelerate the encoding and improve its energy efficiency while posing minor coding efficiency losses.