Authors :
Mahesh Pawaskar; Dr. Gaurav Vijay
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
http://tinyurl.com/mr2avzdm
Scribd :
http://tinyurl.com/4s2cpmfu
DOI :
https://doi.org/10.5281/zenodo.10390264
Abstract :
The most common type of data used globally is
presently video data. The volume of video data has been
rising explosively around the globe as a result of the quick
development of video applications and the rising demand
for higher-quality video services, giving the biggest
challenge to multimedia processing, transmission, and
storage. Video coding by compression has become
somewhat saturated while the compression ratio has
grown in the last three decades. Deep Learning
algorithms offer new possibilities for improving video
coding technologies since they can make data-driven
predictions and learn from vast amounts of unstructured
data. We explore machine learning-based video encoding
optimization in this research, which lays a solid
groundwork for further advancements in video coding.
The video service's designer must choose a suitable video
coding scheme to satisfy criteria like efficiency,
complexity, rate distortion, flexibility, etc. This article
also presents challenges associated with machine learning
video coding optimization. The survey is mainly
presented from two key aspects, first is low complexity
optimization with the help of advanced learning tools,
such as feed-forward CNN, deep RL, and deep NN, and
second is learning-based visual quality assessment (VQA).
Keywords :
Video Coding, Deep Learning, Machine Learning, High-Efficiency Video Coding Standard (HEVC), Versatile Video Coding (VVC), Visual Quality Assessment. (VQA).
The most common type of data used globally is
presently video data. The volume of video data has been
rising explosively around the globe as a result of the quick
development of video applications and the rising demand
for higher-quality video services, giving the biggest
challenge to multimedia processing, transmission, and
storage. Video coding by compression has become
somewhat saturated while the compression ratio has
grown in the last three decades. Deep Learning
algorithms offer new possibilities for improving video
coding technologies since they can make data-driven
predictions and learn from vast amounts of unstructured
data. We explore machine learning-based video encoding
optimization in this research, which lays a solid
groundwork for further advancements in video coding.
The video service's designer must choose a suitable video
coding scheme to satisfy criteria like efficiency,
complexity, rate distortion, flexibility, etc. This article
also presents challenges associated with machine learning
video coding optimization. The survey is mainly
presented from two key aspects, first is low complexity
optimization with the help of advanced learning tools,
such as feed-forward CNN, deep RL, and deep NN, and
second is learning-based visual quality assessment (VQA).
Keywords :
Video Coding, Deep Learning, Machine Learning, High-Efficiency Video Coding Standard (HEVC), Versatile Video Coding (VVC), Visual Quality Assessment. (VQA).