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Data Embedding in SHVC Video Using Threshold-Controlled Block Splitting
Applied Sciences ; 11(11):4850, 2021.
Article in English | ProQuest Central | ID: covidwho-1731903
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.
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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Type of study: Experimental Studies Language: English Journal: Applied Sciences Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Type of study: Experimental Studies Language: English Journal: Applied Sciences Year: 2021 Document Type: Article