  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.
