Improved visual information fidelity based on sensitivity characteristics of digital images

Digital images may lose certain information during transmission or transcoding processes. Since the lost information can influence the visual quality perceived by the human eyes, several quality assessment metrics have been proposed. The structural similarity index (SSIM) and visual information fidelity (VIF) are two of the most common methods that take characteristics of the human perceptual system into account. Although many improved metrics based on SSIM have been developed, the methods related to VIF, which outperforms SSIM-based approaches in certain image databases, have rarely been discussed. This research aims at improving VIF to increase the effectiveness and reduce its computational complexity. The enhanced VIF employs the Haar wavelet transform, log-Gabor filter, and spectral residual approach to emphasize the visual sensitivity in image quality assessment. The experimental results demonstrate the superior performance of the proposed method, when compared to various popular or latest assessment indices.