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

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%.