# Data Embedding in SHVC Video Using Threshold-Controlled Block Splitting
###### 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. SHVC; data embedding; quad-tree block splitting; threshold-controlled +
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the video as well as to navigate between videos and other hypermedia based on the user's interests [2]. In another application, error concealment suppresses interruption caused by abrupt bandwidth changes or packet losses by using data embedding techniques [3]. As one of the fastest growing technologies, machine learning requires huge a volume of data to train the machine learning model of interest. Ground truth, or more generally, metadata, can be inserted into a video as the training data to facilitate automated computer vision-based applications such as object tracking and localization. There are also security concerns over adversarial videos aiming to corrupt the training process [4], which can be addressed by data embedding to ensure that only genuine training videos are utilized. Furthermore, a biometric signature or fingerprint can be embedded into a video so that only authorized users can access the video content [5; 6]. Moreover, a cryptographic hash of a video can be inserted into the video of interest so that the receiver can verify its originality [7]. Even with the recent breakthroughs in network and storage technologies, handling video in the raw (i.e., uncompressed) form is still a challenging task due to the large volume of data in the video. Therefore, most videos are stored and transmitted in the encoded form, each being compliant to specific video coding standards [8]. As an extension of the high efficiency video coding (HEVC) standard [9], the Scalable High Efficiency Video Coding (SHVC) standard [10] encodes a high definition video into a single bit stream with multiple resolutions/qualities. It emerges as an efficient video coding solution for adaptive video streaming applications. The scalability feature is essential, notably when considering the heterogeneity in the transmission environment and decoding devices. Many applications such as smart surveillance and monitoring systems have benefited from this technology. For example, it allows users at different locations to monitor an area in real-time at different display resolutions or frame rates depending on the network condition and constraint of the decoding device. However, the underlying architecture of SHVC differs from the previous scalable video coding standards such as H.264/SVC [11] or MPEG-2 [12].