As Digital videos and images are remarkably expanding over the internet due to the development of blogs, video sharing websites and social network applications, it becomes necessary to protect copyrights of the digital contents to guarantee the ownership identification. Digital watermarking in multimedia has become the most efficient technology used in securing digital data. In practice, the watermarking technique embeds a small copyright message, called a watermark, inside the digital media content to identify the ownership.

Effective techniques in watermarking should consider both imperceptivity and robustness of the algorithm. Imperceptibility refers to the watermark perceptual transparency [1] , while robustness indicates the ability of extracting the embedded watermark from the digital media under intentional or unintentional attacks such as degradation, cropping, filtering or additive noise [1] [2] . Capacity and blindness of the watermarking techniques are two additional issues to be considered as well. Capacity, also referred as data Payload, includes methods that make it possible to embed the majority of information. Blindness of the algorithms implies that the watermark- ing extraction does not need the existence of the original image [3] .

In literature, many watermarking algorithms have been proposed for video, video frames and images. In general, Digital watermarking techniques are classified into two categories: spatial domain watermarking, as in [4] - [6] , and transform domain watermarking, such as in [7] - [10] . In spatial domain methods, watermarking is performed by modifying the pixels values in the host image. However, in frequency domain methods, the water- mark is embedded into transformed coefficients of the image. Frequency domain methods are found to be more robust and complex than spatial domain ones; also they are visually more convenient to human and have higher imperceptibility and capacity [11] .

All kinds of digital media may be protected in order to guarantee ownership and copyrights identification, and hence watermarking can be applied on digital videos, audios, images or documents. In the case of digital videos, watermarking may be applied on both compressed and uncompressed videos. However, applying watermarking on uncompressed videos is much safer than compressed ones because video frames are considered as consecutive and equally spaced still; and hence, watermark will be embedded without any data loss risk. In contrast, watermarked compressed video such as MPEG-compressed bit stream can be converted to another compression standard which may result in modifying or losing the embedded watermark data [12] .

There are many researches on transforming domain watermarking for audio, images, compressed and uncompressed videos. Various proposed algorithms made use of popular transformations such as SVD, DWT, or DCT. Yet, many algorithms combined some of these transformations to form hybrid approaches in order to obtain better embedding and extraction results. In some new researches, SCHUR transform was studied and used in watermarking techniques. Using Schur transform in watermarking embedding and extraction had proved to be a promising area that may compete with other transform domains and make good results for robustness and precision in watermarking.

In [13] , authors presented a non blind hybrid watermarking scheme using SCHUR and SVD transforms. They used Schur in embedding and extraction steps after decomposing the image into 8 × 8 blocks followed by applying SVD on the largest Eigen values. However, in [14] , authors proposed a non blind hybrid technique using SCHUR and Cellular Automata Transform (CAT), and made use of the flexibility of CAT domain and the efficiency of the Schur transform. While in [15] , a blind image watermarking method based on Schur transform was proposed. The image is transformed into Schur transform and then analyzed to find a non-sensitive zone where embedding procedure take place. On the other hand, the proposed algorithm in [16] embeds the watermark in two decomposition of SCHUR transform, which are the D matrix and the U matrix, the technique improves the robustness of the algorithm since the same watermark information is stored twice in two places.

In this paper, a robust, blind digital video watermarking technique is proposed. The proposed technique combined the Schur decomposition with the DWT transform to produce an efficient technique with high robustness and imperceptibility. The rest of this paper is organized as follows: The Discrete Wavelet transform and the Schur decomposition is described in Section 2. In Section 3 the proposed watermarking technique is discussed in details. In Section 4 the experimental results are discussed and Section 6 concludes the proposed technique.

In this section we investigate the robustness of the proposed DWT-Schur technique against different attacks. The cover video clip used for testing has four scenes with 438, 599, 744 and 821 frames respectively. The size for each frame is 352 × 352 pixels. A snapshot from each scene is shown in Figure 5. The watermark is a binary text image with size 363 × 256 as shown in Figure 6. The use of text messages makes it easy to test the robustness. Where we simply read the extracted message and compare it with the original watermark.

To measure the level of robustness and the similarity between the original watermark W and the extracted watermarks W’ the correlation shown in the Equation (7) is used. The correlation may take values between 0 and 1. Where 0 indicates that there is no similarity and 1 indicates that both watermarks are same.

In the Normal Test the proposed technique shows a high imperceptibility where. No visual degradation was noticed in the video clip after embedding the watermark and the similarity between the embedded and extracted watermark are completely similar as shown in Figure 7. The average PSNR for the all watermarked frames was 57.3118. At this PSNR value no quality degradation in the watermarked video was perceived the PSNR is shown in Equation (8).

The robustness for the proposed technique is measured against two types of attacks: standard and frame attacks. Standard attacks include compression, rotation, Gaussian noise, salt & pepper noise, among many others [2] . The video attacks are frames swapping, averaging and dropping. For both types of attacks we measured the similarity between the original and extracted watermarks using the correlation.

Salt & Pepper and Gaussian Noise Addition Attacks The robustness of the proposed technique against salt & pepper and Gaussian noise is acceptable since the watermark is still distinguished and the correlation results are somehow high but the lowest compared with the other attacks which is reasonable since the noise addition attacks affects each pixel in the watermarked frame (the results are shown in Table 3).

Rotation Attacks for the rotation attacks we rotated the watermarked image by three different angels 30, 90 and 180 degrees. The proposed techniques shows high robustness against this attack as you see in Table 4 the watermarks are almost the same as the original with high values of correlation. Hence, the diagonal in S matrix was selected in our technique for watermarking embedding as we discussed before and this is because of the fact that Eigen values in Schur decomposition are highly stable and increase the robustness.

Video Attacks for video attacks we test the robustness for the watermarked scenes by applying frames swap- ing attacks where the fourth and the tenth frames in each watermarked scene are swapped. Then frame averaging video attacks and finally the frame dropping video attacks where we dropped 90% from the watermarked scene and as you see in Table 5 the results are reflected in the extracted watermark where approximately we see 10% of the watermark and the remaining is dropped with the video. This is because the watermark is distributed in all the frames in each video scene.

This paper presented a new technique for blind digital video watermarking. The proposed technique made use of two powerful mathematical transforms: Discrete wavelet transforms (DWT) and Schur decomposition. Experimental results demonstrated the imperceptibility, blindness and robustness of the proposed technique. The watermark was successfully extracted without the existence of the original video and no visual distortion appears in the watermarked video. The technique proved its robustness against a set of standard and video attacks, and succeeded in extracting the watermark with minimum distortion. Using Schur decomposition improved the efficiency of the technique due to the small number of computations compared to other similar mathematical decompositions.