Reduced-reference image quality assessment in reorganized DCT domain

In this paper, a novel reduced-reference (RR) image quality assessment (IQA) is proposed by depicting the subband statistical characteristics in the reorganized discrete cosine transform (RDCT) domain. First, the block-based DCT coefficients are reorganized into a three-level coefficient tree, resulting in ten RDCT subbands. For the intra RDCT subband characteristic, the coefficient distribution of each RDCT subband is modeled by the generalized Gaussian density (GGD) function. The city-block distance (CBD) is employed to measure the modeling error between the actual distribution and the fitted GGD curve. For the inter RDCT subband characteristic, the mutual information (MI) is utilized to depict the dependencies between coefficient pairs in related RDCT subbands. Moreover, a frequency ratio descriptor (FRD) calculated in the RDCT domain is proposed to depict how the image energy distributes among different frequency components. The FRD values computed from both the reference and distorted images are jointly considered to derive a novel mutual masking strategy for simulating the texture masking property of the human visual system (HVS). By considering the GGD modeling of intra RDCT subband, MI of inter RDCT subbands, and FRD of the image, the proposed RR IQA is developed. Experimental results demonstrate that a small number of RR features is sufficient to represent the reference image for the perceptual quality analysis. The proposed method can outperform the state-of-the-art RR IQAs, and even the full-reference (FR) PSNR and SSIM.