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%.
It is beneficial to have adaptive resolution selection during encoding for the viewer's optimal experience in a video delivery scenario. For instance, per-title content-adaptive techniques have been exploited for OTT/VOD delivery. An established solution is to build the selection of better resolution on typical video quality metrics. In this context, this paper first introduces measuring the performance of video quality metrics to accurately predict which encoding resolution is subjectively better suited to a particular scene of interest in a video. Then, with a Random Forest (RF) classifier, a novel, subjectively accurate classifier called RF-based fusion metric is proposed to decide which encoding resolution is best suited to compensate for individual metrics' disparate performance over different quality ranges. The proposed RF classifier encompasses classical video quality metric scores as features and is trained to be closer to subjective experimental results. Performance comparison of this proposed RF-based fusion metric is discussed in comparison to other metrics and subjective experiments.
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