We present a novel objective full-reference image quality assessment metric based on multiscale geometric analysis. The multichannel behavior of the human vision system is emulated by contourlet transform, a perceptual subband decomposition. Not only the contrast-masking effect but also the entropy-masking effect is considered to deal with the visual masking issue. In the error pooling stage, the frequency sensitivity of the HVS is investigated. Nonlinear and linear fusion schemes of subband distortion are compared. Extensive validation experiments are performed on two professional image databases, the LIVE database supplied by the University of Texas and the A57 database supplied by Cornell University. Compared with several state-of-the-art image quality metrics, the proposed metric demonstrates improvement on prediction accuracy and robustness.