Video Codec Using Machine Learning Based on Parametric Orthogonal Filters
Mikhail V. Gashnikov · Optical Memory and Neural Networks · 2023
Abstract The research deals with video encoding using a machine learning-based videoframe approximator. The use of neural networks and hierarchical classifiers is considered in the context of this sort of approximator. Using a machine learning-based hierarchical classifier, the approximator switches at each point of a videoframe between elementary approximators from a predefined set of elementary classifiers. Convolutional filters with parametric orthogonal kernels are used as elementary classifiers. An algorithm for optimizing the hierarchical classifier is considered. The algorithm is based on recursive recalculations of the entropy quality index, which provides a good approximation of the encoded-data size. This sort of videoframe approximator is intended for a video codec using nested representations of videoframes. Real video sequences are used in computational experiments. The results indicate that the use of the videoframe approximator with a hierarchical classifier engaging parametric orthogonal kernels enables a noticeable reduction of the size of the encoded-data array.