A multidimensional image quality measure using singular value decomposition

The important criteria used in subjective evaluation of distorted images include the amount of distortion, the type of distortion, and the distribution of error. An ideal image quality measure should therefore be able to mimic the human observer. We present a new image quality measure that can be used as a multidimensional or a scalar measure to predict the distortion introduced by a wide range of noise sources. Based on the Singular Value Decomposition, it reliably measures the distortion not only within a distortion type at different distortion levels but also across different distortion types. The measure was applied to Lena using six types of distortion (JPEG, JPEG 2000, Gaussian blur, Gaussian noise, sharpening and DC-shifting), each with five distortion levels.