APSIPA Transactions on Signal and Information Processing > Vol 7 > Issue 1

Machine learning for media compression: challenges and opportunities

Industrial Technology Advances

Amir Said, Qualcomm Technologies Inc., USA, said@ieee.org
 
Suggested Citation
Amir Said (2018), "Machine learning for media compression: challenges and opportunities", APSIPA Transactions on Signal and Information Processing: Vol. 7: No. 1, e8. http://dx.doi.org/10.1017/ATSIP.2018.12

Publication Date: 11 Sep 2018
© 2018 Amir Said
 
Subjects
 
Keywords
Machine learningMedia compressionVideo coding
 

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Open Access

This is published under the terms of the Creative Commons Attribution licence.

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In this article:
I. INTRODUCTION 
II. PREDICTION FOR MEDIA COMPRESSION 
III. ADDING TRANSFORMS 
IV. HUMAN PARTICIPATION 
V. RESEARCH PROBLEMS 
CONCLUSIONS 

Abstract

Machine learning (ML) has been producing major advances in several technological fields and can have a significant impact on media coding. However, fast progress can only happen if the ML techniques are adapted to match the true needs of compression. In this paper, we analyze why some straightforward applications of ML tools to compression do not really address its fundamental problems, which explains why they have been yielding disappointing results. From an analysis of why compression can be quite different from other ML applications, we present some new problems that are technically challenging, but that can produce more significant advances. Throughout the paper, we present examples of successful applications to video coding, discuss practical difficulties that are specific to media compression, and describe related open research problems.

DOI:10.1017/ATSIP.2018.12