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CUPSEED - A combined use of prediction syntax elements to embed data in SHVC video.

Authors :
Pang, LieLin
Tew, Yiqi
Wong, KokSheik
Ayub, Mohamad Nizam Bin
Source :
Multimedia Tools & Applications; Apr2021, Vol. 80 Issue 9, p13121-13142, 22p
Publication Year :
2021

Abstract

With the rapid advancement in digital technologies, video rises to become one of the most effective communication tools that continues to gain popularity and importance. As a result, various proposals are put forward to manage videos, and one of them is data embedding. Essentially, data embedding inserts data into the video to serve a specific purpose, including proof of ownership via watermark, covert communication in steganography, and authentication via fragile watermark. However, most conventional methods embed data by using only one type of syntax element defined in the video coding standard, which may suffer from large bit rate overhead, quality degradation, or low payload. Therefore, this work aims to explore the combined use of multiple prediction syntax elements in SHVC for the purpose of data embedding. Specifically, the intra prediction mode, motion vector predictor, motion vector difference, merge mode and coding block structure are collectively manipulated to embed data. The experimental results demonstrate that, in comparison to the conventional single-venue data embedding methods, the combined use of prediction syntax elements can achieve higher payload while preserving the perceptual quality with minimal bit rate variation. In the best case scenario, a total of 556.1 kbps is embedded into the video sequence PartyScene with a drop of 0.15 dB in PSNR while experiencing a bit rate overhead of 7.4% when all prediction syntax elements are utilized altogether. A recommendation is then put forward to choose specific types of syntax element for data embedding based on the characteristics of the video. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13807501
Volume :
80
Issue :
9
Database :
Complementary Index
Journal :
Multimedia Tools & Applications
Publication Type :
Academic Journal
Accession number :
149990426
Full Text :
https://doi.org/10.1007/s11042-020-10359-w