## 3 Proposed Work
### Initial Scaling Factor Generation and Optimization  
The visual quality and robustness of watermarking scheme largely depends on embedding strength parameter (\(\alpha\)). Until now, the majority of DIW schemes choose random ISF (\(\alpha\)) for watermark embedding and the extraction process. Choosing the same random ISF for all image modalities may degrade visual quality. To ensure higher visual quality, the proposed scheme generates ISF adaptively from the cover image using image range texture characteristics. The algorithmic steps for adaptive ISF generation is presented in Algorithm 5. The fuzzy based image texture range filter characteristic is used for generation of ISF adaptively from the cover image. The image range filter defines a neighborhood around the pixel of interest and calculates the statistics for that neighborhood. If the intensities in the image range has more variability, this indicates that there is a distinguished foreground and background in the image. Adaptive generation of ISF is computationally inexpensive. In the proposed scheme, less variability regions are selected for embedding to achieve higher visual quality. For ISF generation, a \(3\times 3\) neighborhood filter function is used. The relation used for generation of range values is in Equation (7):  
\[Range=Maxval\ \ -\ Minval \tag{7}\]  
where Maxval is the maximum intensity and Minval is the minimum intensity values of the selected \(3\times 3\) filter. The proposed scheme generates Maxval and Minval from the selected \(3\times 3\) filter using morphological operations called dilation and erosion, respectively. The dilation operator results in a maximum value and the erosion operator results in a minimum value in a selected filter based on their mask filter. Generation of range filter intensities of the \(3\times 3\) filter is illustrated with the help of an example in Figure 8 using dilation and erosion operators. Algorithmic steps for adaptive generation of ISF from the cover image are shown in Algorithm 5. Furthermore, the process of ISF generation is exhibited with an example in Figure 9.  
Figure 8: Range filter.  
If computational cost is not the major concern, then ISF can be further optimized for achieving higher watermarking characteristics. Nature Inspired Optimization (NIO) algorithms such as Genetic algorithm (GA), Artificial Bee Colony (ABC), or Firefly optimization (FO) are proposed for optimizing ISF. Optimization algorithms are used to find solutions that maximize or minimize some study parameter. NIO algorithms are stochastic metaheuristic based evolutionary algorithms developed by the inspiration of nature suitable for larger search space. Researchers have proposed a number of NIO optimization algorithms based on swarm intelligence (ABC, PSO, ACO, Firefly, etc.) and based on genetic behavior (GA, etc.). Among all these, GA, ABC and FO have good exploration and exploitation capabilities to reach the global optimum at lower time intervals. In the proposed scheme, GA, ABC and FO metaheuristic based evolutionary algorithms are adapted for ISF optimization. The fitness function used for obtaining optimized ISF (\(\alpha\)) is shown in Equation (8):  
\[Fitness\ function=\frac{(PSNR\times SSIM)}{\alpha}+\frac{(NC\times BER)}{\alpha} \tag{8}\]