An integrated approach to image quality: comparative analysis of bilinear and nearest neighbor interpolation

Pixel transitions are critical in image processing, largely depending on interpolation methods to ensure smoothness and clarity. This work focuses on two widely used image interpolation techniques: nearest neighbor interpolation and bilinear interpolation, both implemented using integrated software code. Our methodology enables each interpolation technique to be applied independently, allowing for a direct comparison of their performance. To achieve a thorough evaluation of each interpolation method, we utilize a set of essential quality assessment metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Grayscale Analysis, and Mean Squared Error (MSE). These metrics were selected to provide a balanced assessment of image sharpness, structural accuracy, and overall visual quality. The results of this study offer a detailed analysis of the strengths and limitations of each interpolation technique. These findings are intended to assist researchers and practitioners in selecting the most suitable interpolation method for their specific requirements in the image processing domain. By providing a comparative framework, this work contributes to the field by enhancing methods for assessing and optimizing image quality in digital imaging applications.