Image quality assessment is a basic problem in the multimedia field. A new image quality metric is constructed based on Structural Similarity (SSIM) index, by exploiting Human Visual System (HVS) characteristics. First, considering the masking effects of HVS, the distorted image is preprocessed by a distortion model with the input of the error between it and the original one and just noticeable distortions (JNDs) derived from a human visual model, where Sigmoid function is explored. The process makes the visible errors in the modified distorted image to be more notable. Second, considering the visual attention characteristics of HVS, the image area weight model is designed to quantify the importance of image local regions for the visual quality. The interesting content of an image can be represented by the saliency image, from which the weights of different regions are obtained. Finally, the local SSIM between the modified distorted image and original image is calculated, and the global image quality metric can be expressed by weighting all local quality with the normalized regional weights. Compared with the state-of-the-art image metrics, the proposed metric fits subjective visual quality better in evaluating the local image quality, has better performance in terms of the mean square error, correlation coefficient and other indicators for predicting the subjective image quality, and has a moderate computational complexity, well below the run time of the superior performance metrics.