Ultra high definition (UHD) TV is rapidly replacing high definition (HD) TV but little is known of its effects on human visual attention. However, a clear understanding of this effect is important, since accurate models, evaluation methodologies, and metrics for visual attention are essential in many areas, including image and video compression, camera and displays manufacturing, artistic content creation, and advertisement. In this paper, we address this problem by creating a dataset of UHD resolution images with corresponding eye-tracking data, and we show that there is a statistically significant difference between viewing strategies when watching UHD and HD contents. Furthermore, by evaluating five representative computational models of visual saliency, we demonstrate the decrease in models' accuracies on UHD contents when compared to HD contents. Therefore, to improve the accuracy of computational models for higher resolutions, we propose a segmentation-based resolution-adaptive weighting scheme. Our approach demonstrates that taking into account information about resolution of the images improves the performance of computational models.