Key Standards for Image Quality Evaluation

Key standards for image quality evaluation include Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Image Fidelity. These metrics serve as essential indicators for assessing image quality. For beginners, learning to implement Signal-to-Noise Ratio (SNR) calculation code in MATLAB proves particularly valuable. By employing basic programming techniques, beginners can gain deeper insights into the concepts and principles of objective image evaluation standards, thereby enhancing their capabilities in the field of image processing. The MATLAB implementation typically involves comparing original and processed images using matrix operations. For PSNR calculation, the code would compute the mean squared error between images, then apply the logarithmic formula: PSNR = 10*log10(MAX^2/MSE). MSE implementation involves element-wise squared differences averaged across all pixels. MAE calculation uses absolute differences instead of squared errors. Image fidelity metrics often incorporate correlation coefficients or structural similarity indices. These implementations help demonstrate how mathematical formulas translate into practical image analysis tools, providing hands-on experience with image data manipulation and quality assessment algorithms.