Image Quality Assessment Techniques pn Spatial Domain
Jaspreet Kaur, Divya Jyoti · 2011
Measurement of image quality is important for many image processing applications. Image quality assessment is closely related to image similarity assessment in which quality is based on the differences (or similarity) between a degraded image and the original, unmodified image. There are two ways to measure image quality by subjective or objective assessment. Subjective evaluations are expensive and time-consuming. It is impossible to implement them into automatic real-time systems. Objective evaluations are automatic and mathematical defined algorithms. Subjective measurements can be used to validate the usefulness of objective measurements. Therefore objective methods have attracted more attentions in recent years. Well-known objective evaluation algorithms for measuring image quality include mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). MSE & PSNR are very simple and easy to use. Various objective evaluation algorithms for measuring image quality like Mean Squared Error (MSE), Peak Signal-ToNoise Ratio (PSNR) and Structural Similarity (SSIM) etc. will be studied and their results will be compared