FIFS: A Machine Learning-Based Fast AV1 Interpolation Filter Search
William Kolodziejski, Marcelo Porto, Luciano Volcan Agostini · 2025
AV1 is a codec developed by huge technology companies to be used in current and future commercial video applications. It introduces and improves several tools from its predecessor VP9, designed for various video scenarios. One of the improved tools is the Fractional Motion Estimation (FME), which generates sub-pixel predictors. AV1 employs four sets of interpolation filters, requiring significant computational effort during the Interpolation Filter Search (IFS) to identify the best filter to be used. This work proposes the FIFS, a machinelearning method developed to reduce the processing time of IFS while maintaining minimal impact on coding efficiency. This method achieves over 52% reductions in IFS time, with only a slight increase in BD-BR of$\mathbf{0.14 \%}$. To the best of the authors' knowledge, this is the first work in the literature to propose a machine learning-based approach for the AV1 IFS.