Deep learning in video compression algorithms

Ofer Hadar, Raz Birman

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Deep Neural Networks (DNN) have emerged in recent year as a best-of-breed alternative for performing various classification, prediction and identification tasks in images and other fields of study. In the last few years, various research groups are exploring the option to harness them to improve video coding with the primary purpose of improving video compression rates while retaining same video quality. Evolving Neural Networks based video coding research efforts are focused on two different directions: (1) improving existing video codecs by performing better predictions that are incorporated within the same codec framework, and (2) holistic methods of end-to-end image/video compression schemes. While some of the results are promising and the prospects are good, no breakthrough has been reported as of yet. This chapter provides an overview of state-of-the-art research work, providing examples of few prominent published papers that illustrate and further explain the different highlighted topics in the field of using DNNs for video compression.

Original languageEnglish
Title of host publicationMulti-faceted Deep Learning
Subtitle of host publicationModels and Data
PublisherSpringer International Publishing
Number of pages24
ISBN (Electronic)9783030744786
ISBN (Print)9783030744779
StatePublished - 20 Oct 2021

ASJC Scopus subject areas

  • Computer Science (all)


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