## 1 Introduction
Therefore, a popular steganography approach is to hide the secret data in the edge areas since any distortions that occur because of the embedding process will not be visually clear. The other approach is statistically based. In this approach, the embedder captures the available redundant data in the host signal and then replaces the redundant data with the secret bits. A digital video signal contains two sources of redundancy. The spatial relations between pixels are a source of redundancy. The other source of redundancy is the temporal relation between pixels. The amount of redundant data is related to several attributes such as the motion speed of the objects and the amount of details in each still frame. A video segment that has frames with little details and slow-motion content will have a significant amount of redundant data. Exploiting data redundancy is a key strategy for compression and steganography. The redundant data in a signal can be captured by analyzing the frequency content of the signal. This is done by transforming the signal from the native domain to the frequency domain using a transform technique. The discrete cosine transform (DCT) is a successful transform technique that is widely favored by data-hiding techniques. Similar to the discrete Fourier transform (DFT), the DCT breaks the input signal into its fundamental frequency components. The critical advantage of the DCT over DFT is the strong compaction property of the DCT. For steganography, the DCT has a strong ability to express a signal within a few critical frequency components. The strong compaction property results in having a respectful amount of insignificant frequency components, which can be replaced with the secret bits without affecting the quality of the carrier signal. It has been noticed that the strong compaction property of the DCT becomes stronger with a highly correlated dataset [7]. This means that fewer significant frequency components can hold most of the signal's energy compared to an uncorrelated dataset. Therefore, several recent steganography schemes suggested segmenting the host signal to correlated segments to fully exploit the strong compaction property of the DCT. For example, a recent video steganography scheme has been proposed to extract a temporal vector from each spatial location [8]. The rationale of the proposed technique is that consecutive frames will surely have correlated pixels to develop a valid video segment. This segmentation process has ensured capturing the temporal redundancy to some extent. The work proposed in [8] does not optimally capture the redundant data in a video segment. Therefore, this paper proposes a new video steganography technique that aims to integrate both the temporal and the spatial redundancy by applying the Octree segmentation technique which is a spatiotemporal segmentation algorithm. The Octree segmentation technique can break up a 3D signal into smaller homogeneous 3D signals. The proposed scheme suggests applying the 3D-DCT on each homogeneous 3D signal. The energy of each homogeneous 3D signal will be packed within a few numbers of significant frequency components. Therefore, a respectful amount of insignificant frequency components can be replaced safely with a scaled version of the secret data. Thus, the proposed steganography technique can deal efficiently with the capacity-imperceptibility trade-off problem and hide a substantial number of secret bits while maintaining the quality of the carrier signal. The rest of this paper is organized as follows. Section 2 presents related works in the video steganography field. In Section 3, the proposed Octree steganography technique is described. Comparative experimental analysis is presented in Section 4. Section 5 discusses future plans. Finally, our concluding remarks appear in Section 6.