In this paper, we propose a new no-reference image quality assessment (NR-IQA). The method makes use of local binary patterns (LBP) to label local textures of an image. These labels form a LBP map that can be used to measure the characteristics of image textures (texture map). Then, we compute the histogram of the texture map and weight each LBP label according to its saliency, which is obtained with a visual attention computational model. The weighted histogram is used as input to a regression method that estimates the quality of the image. Experimental results show that the proposed method achieves competitive prediction accuracy and outperforms other state-of-the-art NR-IQA methods. At the same time, the method is simple and reliable, demanding few computational resources, such as memory and processing time.