MATLAB Code Implementation for Calculating Peak Signal-to-Noise Ratio (PSNR)

Calculating the Peak Signal-to-Noise Ratio (PSNR) of an image can be used to evaluate the effectiveness of image steganography. PSNR is an important metric for measuring image quality, representing the ratio between the signal and noise in an image. By computing the PSNR, we can understand the extent to which noise affects the signal in an image, thereby determining whether the steganographic effect is satisfactory. In MATLAB implementation, PSNR calculation typically involves comparing the original image with the processed/steganographic image. The key steps include: 1. Reading both original and steganographic images using imread() function 2. Converting images to double precision for accurate mathematical operations 3. Calculating Mean Squared Error (MSE) between the two images 4. Determining maximum possible pixel value (typically 255 for 8-bit images) 5. Applying the PSNR formula: PSNR = 10*log10(MAX^2/MSE) Higher PSNR values indicate that the signal strength relative to noise is greater, resulting in better image quality. Therefore, calculating PSNR is crucial for assessing the performance of image steganography techniques, particularly when evaluating the visual impact of embedded data. The MATLAB implementation would typically use functions like: - imread() for image input - im2double() for data type conversion - mean2() for matrix mean calculation - Custom code for MSE and PSNR computation This approach provides a quantitative measure to evaluate how well the steganographic process preserves image quality while hiding information.