# Framework for Video Steganography Using Integer Wavelet Transform and JPEG Compression
## 4 Experimental Setup  
The proposed IWT technique is implemented in MATLAB for analysis and validation of experimental results. MATLAB is an encoding and numeric computing platform widely used by researchers to analyze information, build algorithms, and generate models. It is compatible with Windows and requires a minimum of 4 GB RAM. The image acts as secret information and the video acts as a cover for the proposed work. More than one video is taken as a cover file and [PERSON]'s image is taken as a secret image every time. [PERSON] is a standard image, widely used in many image processing applications. The detail of the videos taken as the cover file is as follow in Table 2:  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Video**} & \multirow{2}{*}{\begin{tabular}{c} **Video Size** \\ **in Second** \\ \end{tabular} } & \multirow{2}{*}{**Frame Rate**} & \multirow{2}{*}{\begin{tabular}{c} **Aspect** \\ **Ratio** \\ \end{tabular} } & \multicolumn{2}{c}{**Total**} & \multirow{2}{*}{\
\begin{tabular}{c} **Frame** \\ **Frames** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Frame** \\ **Height** \\ \end{tabular} } & \multirow{2}{*}{**Data Rate**} & \multirow{2}{*}{**Bit Rate**} \\ \hline Video 1 & 18 & 27 & 4:3 & 534 & 470 & 810 & 2437 & 2,453,551 \\ Video 2 & 20 & 31 & 3:3 & 637 & 488 & 812 & 2166 & 2,256,343 \\ Video 3 & 30 & 52 & 20:16 & 1910 & 255 & 349 & 235 & 225,436 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detail of Cover Video.  
All the videos have different properties such as size, frame rate, aspect ratio, the total number of frames, frame height, frame width, data rate, bit rate, etc. with the change in properties of cover video files value of performance matrices also varies. We have taken three different videos to validate our experimental results. Same secret image Lena is embedded in all the three different videos to analyse the experimental results.  
Commonly used performance metrics for evaluation of research work are explained below:  
**PSNR:** It is the performance metric used to check the excellence of the research work. It can be demarcated as the relation between the maximum possible powers of the original signal to the power of distorted noise. PSNR value is calculated in dB. PSNR value higher than 35 db represents the good visual quality of the stego file [26].  
**MSE:** It is the amount of error present between the original and carrier file. A small difference between the original and stego file means no error is present. It can be calculated in MATLAB by using a built-in function [27]. It is calculated in dB\({}^{2}\).  
**SSIM:** It is used to find the resemblance between the original and stego file. It is more efficient compared to PSNR and MSE matrices. It is the full reference metric, in other words, the measurement or prediction of image quality is based on an initial uncompressed reference. Its value varies from 0 to 1. '0' represents the poor quality of the stego file and '1' represents the excellent quality of the stego file [26].  
**CC:** It measures the strength of the relationship between the original and the stego file. Its value varies from 0 to 1. Zero value represents the strongest relationship or no statistical change in two files. Value 1 represents a weak relationship or exposure of secret information [20; 27].