Optimal Video Compression using Pixel Shift Tracking

TL;DR

This paper introduces R²S, a novel video compression method that leverages pixel shift tracking to identify and remove redundancies between frames, aiming to improve storage efficiency.

Contribution

The paper proposes a new redundancy removal technique using pixel shift tracking, adaptable across ML models, to enhance video compression efficiency.

Findings

Effective identification of redundant pixels between frames

Improved video storage efficiency demonstrated

Applicable across various ML algorithms

Abstract

The Video comprises approximately ~85\% of all internet traffic, but video encoding/compression is being historically done with hard coded rules, which has worked well but only to a certain limit. We have seen a surge in video compression algorithms using ML-based models in the last few years and many of them have outperformed several legacy codecs. The models range from encoding video end to end using an ML approach or replacing some intermediate steps in legacy codecs using ML models to increase the efficiency of those steps. Optimizing video storage is an essential aspect of video processing, so we are proposing one of the possible approaches to achieve it is by avoiding redundant data at each frame. In this paper, we want to introduce the approach of redundancies removal in subsequent frames for a given video as a main approach for video compression. We call this method Redundancy Removal using Shift (R\textsuperscript2S). This method can be utilized across various Machine Learning model algorithms, and make the compression more accessible and adaptable. In this study, we have utilized a computer vision-based pixel point tracking method to identify redundant pixels to encode video for optimal storage.