4. Noise removal selective filter

One of the challenging aspects in video encoding and watermarking is the additive noise that results in distorted video streams. The nature of the additive noise depends primarily on the source of this noise. Not only the additive noise tends to distort the visual quality of the video in question, but it also has its noticeable impacts on the watermarking process. One type of noises that is common in video processing techniques is the salt-and-pepper (S&P) noise. This type of noise could be added to the video frames during the transmission process when the communication channels, in a sense, are noisy, or it could be a result of the hardware-generated errors during the encoding and decoding processes. Removing the noise without disturbing the watermarking process on the one hand and preserving the visual qualities on the other hand is a challenging process. As far as the watermarking process is concerned, it is useful to check the effects of both the additive noise and the removal process on our data hiding process. Many methods were proposed to eliminate the noise or enhance the visual appearance of the images [18, 19]; these methods depend mainly on the idea of median filters. The normal median filters, for example, which are used to eliminate the salt-and-pepper noise in images, do in fact filter the whole image regardless of the presence or absence of the noise in a certain area. This process reduces the original resolution of the image to a great extent in such a way that the qualities of high-definition (HD) videos are lost. This means that our watermarking process would not achieve the visual quality

and all the other extracted watermarks in the set; then the average cross-correlation parameter is evaluated. The same process is done for all the watermarks in the set. A set that includes each extracted watermark and its corresponding average correlation value is established. Then, by establishing a threshold value h for the average cross-correlations, the extracted watermarks that do not achieve the threshold test are excluded from the new set W1. The final extracted watermark we can be evaluated by performing an averaging process on the watermarks in the set

Doing an averaging process is attributed to the fact that binary sets follow specific statistical pattern. The correlation coefficient R between any two arbitrary matrices A and B is given in Equation 4; the mean value of a binary image A is at the same time the expected value of A or E(A). Assuming that at the input, the probability of 1 is p<sup>1</sup> and that the probability of flipping of the value is p as shown in

Moreover, the probability of having 1 at the output B ¼ p<sup>1</sup> ∗ ð Þþ 1 � p 1 � p<sup>1</sup>

and by taking Equation 6 into consideration, this equation can be rewritten as

we ¼ Ave Wf g<sup>1</sup> (5)

A ¼ E Að Þ¼ p<sup>1</sup> (6)

B ¼ E Bð Þ¼ E Að Þþ ð Þ 1 � 2 ∗ E Að Þ ∗ p (7)

∗ p

W1, where

Figure 6.

Figure 5.

Expected values of the input and output binary images.

3D plots of the cross-correlation matrices of two extracted watermarks.

Wavelet Transform and Complexity

Figure 6, then

64

condition. In this research, a noise detection process that depends on the absolute differences between a pixel aij and its surrounding pixels is proposed. In order to enhance the detection process, the variance of the pixels in the surrounding window is calculated. This step is important because of false detections, especially at edged and textured details of the image where the absolute difference value could be high, while the region is noise-free. This method takes into account the fact that such variances are dramatically high at these locations. However, this is not the case around noisy pixels in general where some sort of consistency is there. The proposed method for noise detection and elimination process involves the following steps:


• If AM is greater than t and V is less than the variance threshold, then do the elimination process by replacing the noisy pixel by the median of the

• Otherwise, do nothing. In this case, either there is no noise, or the pixel in question is on one of the edges of the image, and nothing should be done accordingly. Smooth and textured images perform differently with respect to noise; it is easier to remove noise, specifically salt-and-pepper

The arithmetic mean (AM) threshold is a user-defined value between the minimum and maximum pixel values (0.255) which are used to distinguish an informative pixel from a noisy one. On the other hand, the variance V can take larger values, and its threshold value can be determined accordingly. In fact, its value depends on the images themselves whether they were textured or smooth ones. The original Akiyo frame, a noisy version of this frame with salt-and-pepper noise of 2% density, and the same frame after the denoising process are shown in Figure 8. Figure 9 shows the peak signal-to-noise ratio (PSNR) values of the noisy and denoised versions of the standard videos: Foreman, Akiyo, Football, BasketballDrill,

In this section we demonstrate the performance of our algorithm using our proposed method on different standard videos with and without HEVC process, under different attacks. Furthermore, it will be compared with the method in [20]. Watermarked and unwatermarked versions of a frame of BasketballDrill video (832 480 pixels) are shown in Figure 10. The embedded and extracted watermarks of size 15 26 are shown in Figure 11; in fact, they are enlarged for

Our algorithm performance will be evaluated in terms of PSNR between the original and the watermarked videos and the normalized correlation (NC) between the original and the extracted watermarks for the standard videos: Foreman, Akiyo, Football, BasketballDrill, and BasketballDrive. For the CIF (352 288) videos, a 9 11 watermark was used, while for the other two videos, the watermark in Figure 11(a) was used. In these tests, 100 frames were watermarked. Figure 12 shows the NC of the extraction process; moreover, Figure 13 shows the enhanced

surrounding pixels in the window.

DWT-Based Data Hiding Technique for Videos Ownership Protection

noise from smooth images.

PSNRs of standard videos before and after denoising process.

DOI: http://dx.doi.org/10.5772/intechopen.84963

and BasketballDrive.

Figure 9.

illustration purposes.

67

5. Experimental results

DWT-Based Data Hiding Technique for Videos Ownership Protection DOI: http://dx.doi.org/10.5772/intechopen.84963

Figure 9.

condition. In this research, a noise detection process that depends on the absolute differences between a pixel aij and its surrounding pixels is proposed. In order to enhance the detection process, the variance of the pixels in the surrounding window is calculated. This step is important because of false detections, especially at edged and textured details of the image where the absolute difference value could be high, while the region is noise-free. This method takes into account the fact that such variances are dramatically high at these locations. However, this is not the case around noisy pixels in general where some sort of consistency is there. The

proposed method for noise detection and elimination process involves the

whether the pixel aij is informative or corruptive.

computed and denoted as V.

respective thresholds on the other side:

1. For each pixel aij, a sub-window of size 3x3 around this pixel is taken.

2. The absolute differences between the pixel aij and the surrounding pixels are

3. The arithmetic mean (AM) of the calculated differences for a given pixel aij is computed. The AM is then compared with a threshold value t to detect

4.The 3x3 pixel window is converted to an array, and then it will be arranged in an ascending order. The largest and the smallest values will be eliminated. This will help in removing other noisy pixels in the surrounding window. The resulting array will be denoted L. The variance of the pixels in the array L is

5. A comparison will be performed between AM and V on one side and their

(a) Original Akiyo frame; (b) 2% S&P noisy Akiyo frame; and (c) the denoised frame.

following steps:

Wavelet Transform and Complexity

calculated.

Figure 8.

66

PSNRs of standard videos before and after denoising process.


The arithmetic mean (AM) threshold is a user-defined value between the minimum and maximum pixel values (0.255) which are used to distinguish an informative pixel from a noisy one. On the other hand, the variance V can take larger values, and its threshold value can be determined accordingly. In fact, its value depends on the images themselves whether they were textured or smooth ones. The original Akiyo frame, a noisy version of this frame with salt-and-pepper noise of 2% density, and the same frame after the denoising process are shown in Figure 8. Figure 9 shows the peak signal-to-noise ratio (PSNR) values of the noisy and denoised versions of the standard videos: Foreman, Akiyo, Football, BasketballDrill, and BasketballDrive.
