**6. The proposed watermarking scheme**

The proposed watermarking scheme is a blind quantization-based scheme. A block diagram detailing its steps is shown in **Figure 1**.

### **6.1 Watermark embedding**


$$0.01 < a < 0.1 \text{ and } \mathbf{t} \mathbf{2} > \mathbf{t} \mathbf{1} > \mathbf{t}. \tag{6}$$


#### **Figure 1.**

*The proposed image watermarking scheme.*

The quantization process is done as shown in Eq. (7):

$$\begin{aligned} \text{If } &= \mathbf{1} \text{ and } > \mathbf{0} \text{, then } = \mathbf{t2} - \mathbf{X1}, \\ \text{If } &= \mathbf{0} \text{ and } > \mathbf{0} \text{, then } = \mathbf{t1} + \mathbf{X1}, \\ \text{If } &= \mathbf{1} \text{ and } < \mathbf{0} \text{, then } = -\mathbf{t2} + \mathbf{X1}, \\ \text{If } &= \mathbf{0} \text{ and } < \mathbf{0} \text{, then } = -\mathbf{t1} - \mathbf{X1}, \end{aligned}$$

degree of tolerance to the system against attacks, i.e., the extraction of watermark bits from the selected wavelet coefficients is done using Eq. (8).

If <ð Þ t1 þ t2 *=*2*,*the recovered watermark bit is 0*:*

Then the correlation process is applied between the recovered watermark and the original watermark, obtained via the secret key, just only in the locations of the

The quantization levels are calculated using a method dependent on the image content, and then round off the value of pixels to the nearest quantization level. Using this method, the number of values transmitted over the channel is minimized. HEAD is a quantization method in which the transmitted values are reduced by mapping the values of image pixels to a finite number of quantization levels.

1. First of all, get the histogram of the output, and then the area under the

determined by the number of these slices. Both are equal.

**7.1 Proposed DWT-HEAD watermarking method**

and the watermarked image is obtained.

histogram is divided into a number of vertical slices with equal areas. A width of each slice is inversely proportional to its height. Quantization levels are

2. The midpoint value which is found on the width of each slice is considered as a

3. This is called a nonuniform quantization where the density of the quantization levels increases with increasing the probability of the occurrence of the pixel

4.We mapped all the pixel values that lie within the width of a slice to the quantization level that is represented by the midpoint of this slice.

The steps of watermark embedding can be summarized as follows:

1. The host image is transformed into the wavelet domain; three-level

Daubechies wavelet with filters of length 4 is used. The coefficients of HL3 coefficients are watermarked using HEAD quantization using two quantization

2. Each of the selected wavelet coefficients is quantized. After all the selected coefficients are quantized, the inverse discrete wavelet transform is applied,

The watermark detection process can be shown in **Figure 3**.

**7. The histogram of equal-area division quantization method**

selected coefficients.

*Blind Wavelet-Based Image Watermarking DOI: http://dx.doi.org/10.5772/intechopen.88131*

**in watermarking**

quantization level.

*7.1.1 Watermark embedding*

levels t1 and t2.

**105**

value.

Process of HEAD quantization [11]:

If ≥ ð Þ t1 þ t2 *=*2*,*the recovered watermark bit is 1 (8)

where is the watermark bit corresponding to, and is the watermarked wavelet coefficient. The parameter x1 narrows the range between the two quantization levels t1 and t2 in order to perform a robust oblivious detection. **Figure 2** shows the watermark embedding in a positive wavelet coefficient.

5. After all the selected coefficients are quantized, the inverse discrete wavelet transform (IDWT) is applied, and the watermarked image is obtained.
