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A novel information hiding method for H.266/VVC based on selections of luminance transform and chrominance prediction modes

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conference contribution
posted on 07.10.2021, 11:33 by Xiyao Liu, Kaiyue Shi, Aihua Li, Hao Zhang, Jiang Ming, Hui FangHui Fang
This paper proposes a novel information hiding method designed for H.266/Versatile Video Coding (VVC) compressed video streams. In this work, we explore two exclusive tools in H.266/VVC standard, named Multiple Transform Selection (MTS) and Cross-component linear model (CCLM), to embed information. These two tools are utilized to preserve high video reconstruction quality and compression efficiency as well as enhance embedding capacity. In specific, MTS is for embedding information into luminance blocks by modifying the selections of coding transforms. Comparing with other tools, MTS has less significant impact on compression quality and efficiency. In addition, CCLM is further used to embed information into chrominance blocks to further enlarge the embedding capacity with little impact on the other two metrics. To our best knowledge, it is the first information hiding method exclusively designed for H.266/VVC. Experimental results show that our proposed information hiding method ensures high embedding capacity, remarkable video reconstruction quality and insignificant impact on compression efficiency, which achieves a better overall performances comparing to existing methods for compressed video.

Funding

National Natural Science Foundation of China (61602527)

Natural Science Foundation of Hunan Province, China (2020JJ4746, 2017JJ3416)

History

School

  • Science

Department

  • Computer Science

Published in

2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Pages

3158 - 3163

Source

2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

24/07/2021

Publication date

2022-01-06

Copyright date

2021

ISBN

9781665442077

eISSN

2577-1655

Language

en

Location

Virtual

Event dates

17th October 2021 - 20th October 2021

Depositor

Dr Hui Fang. Deposit date: 24 July 2021