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.

利用机器学习模型对视频进行压缩，通过移除连续帧中的冗余数据来提高视频存储的效率。采用计算机视觉方法追踪冗余像素点，以实现视频的优化编码和存储。

使用像素位移跟踪的最佳视频压缩

Optimal Video Compression using Pixel Shift Tracking

Implicit neural representations (INRs) have emerged as a promising approach
for video storage and processing, showing remarkable versatility across various
video tasks. However, existing methods often fail to fully leverage their
representation capabilities, primarily due to inadequate alignment of
intermediate features during target frame decoding. This paper introduces a
universal boosting framework for current implicit video representation
approaches. Specifically, we utilize a conditional decoder with a
temporal-aware affine transform module, which uses the frame index as a prior
condition to effectively align intermediate features with target frames.
Besides, we introduce a sinusoidal NeRV-like block to generate diverse
intermediate features and achieve a more balanced parameter distribution,
thereby enhancing the model's capacity. With a high-frequency
information-preserving reconstruction loss, our approach successfully boosts
multiple baseline INRs in the reconstruction quality and convergence speed for
video regression, and exhibits superior inpainting and interpolation results.
Further, we integrate a consistent entropy minimization technique and develop
video codecs based on these boosted INRs. Experiments on the UVG dataset
confirm that our enhanced codecs significantly outperform baseline INRs and
offer competitive rate-distortion performance compared to traditional and
learning-based codecs.

通过引入条件解码器与熵最小化技术，该研究提出了一种通用增强框架，成功提升了隐式神经表示在视频存储与处理中的效果，并在多个基线模型上展现了优越的重建质量和收敛速度，为视频回归、修复和插值任务提供了一种强大的技术。