Manufacturers of commercial display devices continuously try to improve the perceived image quality of their products. By applying postprocessing techniques on the incoming signal, they aim to enhance the quality level perceived by the viewer. These postprocessing techniques are usually applied globally over the whole image but may cause side effects, the visibility and annoyance of which differ with local content characteristics. To better understand and utilize this, a three-phase experiment was conducted where observers were asked to score images that had different levels of quality in their regions of interest and in the background areas. The results show that the region of interest has a greater effect on the overall quality of the image than the background. This effect increases with the increasing quality difference between the two regions. Based on the subjective data we propose a model to predict the overall quality of images with different quality levels in different regions. This model, which is constructed on empirical bases, can help craft weighted objective metrics that can better approximate subjective quality scores.