Phase based image quality assessment

Image quality assessment plays a crucial role in many applications. Since the ultimate receiver in most of the image processing environments are humans, objective measures of quality that correlate with subjective perception are actively sought. Limited success has been achieved in deriving robust quantitative measures that can automatically and efficiently predict perceived image quality. The majority of structural similarity techniques are based on aggregation of local statistics within a local window. The choice of right window sizes to produce results compatible with visual perception is a challenging task with these methods. This paper introduces an intuitive metric that exploits the dominance of Fourier phase over magnitude in images. The metric is based on cross correlation of phase images to assess the image quality. Since the phase captures structural information, a phase-based similarity metric would best mimic the visual perception. With the availability of multi-dimensional Fourier and wavelet transforms, this metric can be directly used to assess quality of multi-dimensional images.