Fast VVC Angular Intra-Prediction for 360° Videos Based on Decision Trees

• Ramiro Viana UFPel
• Iago Storch UFPel
• Rogério Rosado UFPel
• Marcelo Porto UFPel
• Guilherme Corrêa UFPel
• Luciano Agostini UFPel

DOI: https://doi.org/10.5753/webmedia.2025.16075

Resumo

The rapid growth of immersive media has positioned 360° videos as a key component in applications such as virtual reality, remote education, tourism, and interactive entertainment. But the massive data volume required to represent 360° videos imposes significant computational challenges to deliver this type of content across the high diversity of current and future devices with support for multimedia. Then, highly efficient video encoding algorithms are required in this scenario. Versatile Video Coding (VVC) is the current state-of-the-art standard for video compression, offering specialized tools for efficient 360° video coding. This paper introduces a Machine Learning-based approach to reduce the computational effort of VVC Angular Intra-Prediction (AIP) tool when encoding 360° video content. The proposed method uses a Decision Tree model to adaptively skip vertical prediction modes in the AIP process. Experimental results show an average encoding time reduction of 10.11% with only a 0.66% impact on coding efficiency. To the best of our knowledge, this is the first work to explore the use of Machine Learning to reduce the computational effort of AIP for 360° videos.

Palavras-chave: 360° videos, VVC, Angular Intra-Prediction, Machine Learning

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