Machine learning-based energy consumption modeling and comparing of H.264 and Google VP8 encoders

Advancement of the prediction models used in a variety of fields is a result of the contribution of machine learning approaches. Utilizing such modeling in feature engineering is exceptionally imperative and required. In this research, we show how to utilize machine learning to save time in research experiments, where we save more than five thousand hours of measuring the energy consumption of encoding recordings. Since measuring the energy consumption has got to be done by humans and since we require more than eleven thousand experiments to cover all the combinations of video sequences, video bit rate, and video encoding settings, we utilize machine learning to model the energy consumption utilizing linear regression. VP8 codec has been offered by Google as a free video encoder in an effort to replace the popular H.264 video encoder standard. This research model energy consumption and describes the major differences between H.264/AVC and VP8 encoders based on of energy consumption and performance through experiments that are machine learning-based modeling. Twentynine uncompressed video segments from a standard data-set are used, with several sizes, details, and dynamics, where the frame sizes ranging from QCIF(176x144) to 2160p(3840x2160). For fairness in comparison analysis, we use seven settings in VP8 encoder and fifteen types of tuning in H.264/AVC. The settings cover various video qualities. The performance metrics include video qualities, encoding time, and encoding energy consumption.