Machine-learning-based tuning of encoding parameters for UGC video coding optimizations

Gaurang Chaudhari; Igor Koba; Ioannis Katsavounidis; Harikrishna Reddy

doi:10.1117/12.2595404

1 August 2021 Machine-learning-based tuning of encoding parameters for UGC video coding optimizations

Gaurang Chaudhari, Igor Koba, Ioannis Katsavounidis, Harikrishna Reddy

Proceedings Volume 11842, Applications of Digital Image Processing XLIV; 1184208 (2021) https://doi.org/10.1117/12.2595404
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Abstract

In the era of COVID-19 pandemic, videos are very important to the billions of people staying and working at home. Two-pass video encoding allows for a refinement of parameters based on statistics obtained from the first pass. Given the variety of characteristics in user-generated content, there is opportunity to make this refinement optimal for this type of content. We show how we can replace the traditional models used for rate control in video coding with better prediction models with linear and nonlinear model functions. Moreover, we can utilize these first-pass statistics to further refine the traditional encoding recipes that are typically used for all input video sequences. Our work can provide much-needed bitrate savings for many different encoders, and we highlight it by testing on typical Facebook video content.

Conference Presentation

Citation Download Citation

Gaurang Chaudhari, Igor Koba, Ioannis Katsavounidis, and Harikrishna Reddy "Machine-learning-based tuning of encoding parameters for UGC video coding optimizations", Proc. SPIE 11842, Applications of Digital Image Processing XLIV, 1184208 (1 August 2021); https://doi.org/10.1117/12.2595404

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