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Communication Dans Un Congrès Année : 2020

A Case Study of Machine Learning Classifiers for Real-Time Adaptive Resolution Prediction in Video Coding

Résumé

At lower bit-rate encoding video in real-time with a reasonable viewing quality is challenging. Content adaptive per-title encoding is usually leveraged for OTT/VOD delivery by selecting the optimal resolutions and qualities of a given video using multiple encodings. Built on such powerful resolution selection principles, this paper introduces an on the fly resolution prediction without requiring multiple encoding with the help of machine learning which is suitable for real-time video delivery. Two machine learning networks are defined based on the resolution of the previous decision period. Three types of machine learning classifiers: weighted SVM, Random Forests (RF), and custom-designed Multi-Layer Perceptron (MLP) are tested. Suitability of classifiers for real-time resolution prediction is discussed based on the accuracy, BDrate performances, and impact of misclassification on encoding performance and hardware implementability. The proposed solution offers a promising average bit-rate savings upto 12.6%.
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Dates et versions

hal-03186419 , version 1 (31-03-2021)

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Madhukar Bhat, Jean-Marc Thiesse, Patrick Le Callet. A Case Study of Machine Learning Classifiers for Real-Time Adaptive Resolution Prediction in Video Coding. 2020 IEEE International Conference on Multimedia and Expo, Jul 2020, Virtual Conference, United Kingdom. pp.1-6, ⟨10.1109/ICME46284.2020.9102934⟩. ⟨hal-03186419⟩
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