Perceptual image quality assessment using a normalized Laplacian pyramid

We present an image quality metric based on the transformations associated with the early visual system: local luminance subtraction and local gain control. Images are decomposed using a Laplacian pyramid, which subtracts a local estimate of the mean luminance at multiple scales. Each pyramid coefficient is then divided by a local estimate of amplitude (weighted sum of absolute values of neighbors), where the weights are optimized for prediction of amplitude using (undistorted) images from a separate database. We define the quality of a distorted image, relative to its undistorted original, as the root mean squared error in this “normalized Laplacian” domain. We show that both luminance subtraction and amplitude division stages lead to significant reductions in redundancy relative to the original image pixels. We also show that the resulting quality metric provides a better account of human perceptual judgements than either MS-SSIM or a recently-published gain-control metric based on oriented filters.