# Enhanced Dual Reversible Data Hiding Using Combined Approaches
## 4 Experimental Results  
In this section, we evaluate the performance of the proposed dual image-based RDH method by analyzing three key aspects: data hiding capacity, the quality of the marked images, and the ability to restore the original images. To this end, we selected nine test images from the USC-SIPI (University of Southern California--Signal and Image Processing Institute) standard dataset [44], which is widely used as a benchmark in steganography and image processing research. As illustrated in Figure 3, the experiments utilized eight \(512\times 512\) images, including Baboon, Barbara, Boat, Goldhill, Airplane, Peppers, Tiffany, Zelda, and Elaine. The selected images were chosen to reflect diverse texture and frequency characteristics. For example, Baboon, Lena, and Barbara contain rich high-frequency components and complex textures, while Peppers, Goldhill, and Airplane include relatively smooth regions and well-defined edges. This ensures that the proposed data hiding method is evaluated across various image patterns to verify its consistency in performance. Additionally, we used grayscale images instead of color (RGB) images to assess the proposed method, as it operates on bitmap (pixel-based) images. Grayscale images simplify the analysis by using a single intensity channel, whereas color images require independent data embedding for each RGB channel, introducing additional complexity. Therefore, the evaluation focuses on the fundamental performance of the method, while its applicability to color images requires further analysis. Although the study is based on nine test images, the selected dataset consists of a representative mix of natural images, structured patterns, and complex textures, aligning with the standard benchmarks widely used in steganography and data hiding research. Thus, the experimental results are not limited to specific images but reflect the method's performance across diverse image types. Furthermore, the proposed method operates on a block-wise basis, making it independent of image resolution. While the experiments were conducted using \(512\times 512\) standard-resolution images, the method can be applied to high-resolution images such as 5 MP medical images, 50 MP smartphone images, 100 MP pathological images, and 1000 MP satellite images in the same manner. Embedding capacity is typically expressed as the number of bits per pixel (bpp) in an image, denoted as 1 bpp. The term bpp indicates how many bits can be embedded within each pixel. An embedding capacity of 1 bpp signifies that one bit can be hidden in each pixel, making the total amount of data that can be concealed directly proportional to the total number of pixels in the image. For instance, if the data are embedded at 1 bpp in an image with a resolution of \(512\times 512\), the total embedding capacity is 262,144 bits (equivalent to 32,768 bytes or 32 KB). The embedding capacity is generally computed using the following formula (Equation (13)):  
\[ER(bits)=\frac{\text{embedded bits}}{\text{total number of pixels in the images}}bpp \tag{13}\]  
Figure 3: Test images: (**a**) Pepper, (**b**) Airplane, (**c**) Boat, (**d**) Goldhill, (**e**) Couple, (**f**) Baboon, (**g**) Zelda, (**h**) Barbara, and (**i**) Elaine (\(512\times 512\)). Using the above formula, we can calculate how much data can be hidden depending on the size of the image and the \(bpp\) value. For example, if data hiding is performed at 1 bpp and the image resolution is \(m\times n\), the embedding capacity will be \(m\times n\) bits. The quality of the marked image, in which data are hidden, is very important in data hiding. In data hiding, the PSNR [27] evaluation of the marked image is a key metric that measures the similarity between the original image and the image after the data have been hidden. The PSNR is used to quantitatively assess the visual quality degradation that occurs in the marked image.