journal article·2021-05-25Open access

Data Embedding in SHVC Video Using Threshold-Controlled Block Splitting

LieLin Pang, KokSheik Wong, Yiqi Tew, Susanto Rahardja

Applied Sciences· Volume 11· Issue 11

Abstract

With the increasing number of video applications, it is essential to resolve issues such as ineffective search of video content, tampered/forged video content, packet loss, to name a few. Data embedding is typically utilized as one of the solutions to address the aforementioned issues. One of the important requirements of data embedding is to maximize embedding capacity with minimal bit rate overhead while ensuring imperceptibility of the inserted data. However, embedding capacity varies depending on the video content and increasing the embedding capacity usually leads to video quality degradation. In this work, a threshold-controlled block splitting technique is proposed for embedding data into SHVC video. Specifically, the embedding capacity can be increased by coding the host video by using more small blocks, which can be achieved by tuning a threshold-controlled parameter in the rate distortion optimization process. Subsequently, the predictive syntax elements in both intra and inter-coded blocks are jointly utilized to embed data, which ensures that data can be embedded regardless of the prediction mode used in coding a block. Results suggest that the proposed method can achieve a trade-off between the increase in embedding capacity and bit rate overhead while maintaining video quality. In the best case scenario, the sequence PartyScene can embed 516.9 kbps with an average bit rate overhead of +7.0% for the Low Delay P configuration, while the same video can embed 1578.6 kbps with an average bit rate overhead of +2.9% for the All Intra configuration.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Video Coding and Compression Technologies · Image and Video Quality Assessment · Advanced Steganography and Watermarking Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia

School of Information Technology, Monash University Malaysia, Subang Jaya 47500, Malaysia

Faculty of Computing and Information Technology, Tunku Abdul Rahman University College, Kuala Lumpur 53300, Malaysia

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

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