New image quality metric using derivative filters and compressive sensing
D.-O Kim, Rae‐Hong Park, J. W. Lee · 2010
In this paper, we propose a new image quality metric using derivative filters in the context of compressive sensing (CS) that represents a sparse or compressible signal with a small number of measurements. In general, an arbitrary image is not sparse or compressible, however, its derivative image is compressible. In this paper, derivative images are obtained using first- and second-order derivative filters such as Sobel operators and Laplacian of Gaussian filters. Each derivative image of the reference and distorted images is measured via CS. Measurements of derivative images are compared for evaluating the image quality. Experiments with the laboratory for image and video engineering database show the effectiveness of the proposed method.