Dynamically Expanded CNN Array for Video Coding

TL;DR

This paper introduces a dynamic CNN array that adapts to video length, enhancing the quality and compression efficiency of standard video codecs by applying machine learning techniques.

Contribution

It proposes a novel method of training multiple CNN parameter sets for short video segments, dynamically expanding to improve video coding performance.

Findings

Improved video quality with CNN refinement.

Enhanced compression efficiency.

Dynamic expansion adapts to any video length.

Abstract

Video coding is a critical step in all popular methods of streaming video. Marked progress has been made in video quality, compression, and computational efficiency. Recently, there has been an interest in finding ways to apply techniques form the fast-progressing field of machine learning to further improve video coding. We present a method that uses convolutional neural networks to help refine the output of various standard coding methods. The novelty of our approach is to train multiple different sets of network parameters, with each set corresponding to a specific, short segment of video. The array of network parameter sets expands dynamically to match a video of any length. We show that our method can improve the quality and compression efficiency of standard video codecs.