Hybrid No-Reference Natural Image Quality Assessment of Noisy, Blurry, JPEG2000, and JPEG Images
In this paper, we propose a new image quality assessment method based on a hybrid of curvelet, wavelet, and cosine transforms called hybrid no-reference (HNR) model. From the properties of natural scene statistics, the peak coordinates of the transformed coefficient histogram of filtered natural images occupy well-defined clusters in peak coordinate space, which makes NR possible. Compared to other methods, HNR has three benefits: 1) It is an NR method applicable to arbitrary images without compromising the prediction accuracy of full-reference methods; 2) as far as we know, it is the only general NR method well suited for four types of filters: noise, blur, JPEG2000, and JPEG compression; and 3) it can classify the filter types of the image and predict filter levels even when the image is results from the application of two different filters. We tested HNR on very intensive video image database (our image library) and Laboratory for Image & Video Engineering (a public library). Results are compared to the state-of-the-art methods including peak SNR, structural similarity, visual information fidelity, and so on.
noise blur jpeg jpeg2000 curvelet wavelet discrete cosine transform (dct) image quality assessment (iqa) log probability density function (pdf) natural scene statistics (nss) no reference (nr)