No abstract

Contemporary game engines offer an outstanding graphics quality but they are not free from visual artifacts. A typical example is aliasing, which, despite advanced antialiasing techniques, is still visible to the game players. Essential deteriorations are the shadow acne and peter panning responsible for deficiency of the shadow mapping technique. Also Z-fighting, caused by the incorrect order of drawing polygons, significantly affects the quality of the graphics and makes the gameplay more difficult. In this work, we propose a technique, in which visibility of deteriorations is uncovered by the objective image quality metrics (IQMs). We test the efficiency of a simple mathematically based metric and advanced IQMs: a Spatial extension of CIELAB (S-CIELAB), the Structural SIMilarity Index (SSIM), the Multiscale Structural SIMilarity Index (MS-SSIM), and the High Dynamic Range Visual Difference Predictor-2 (HDR-VDP-2). Additionally, we evaluate the Color Image Difference (CID) metric, which is recommended to detect the differences in colors. To find out which metric is the most effective for the detection of the game engine artifacts, we build a database of manually marked images with representative set of artifacts. We conduct subjective experiments in which people manually mark the visible local artifacts in the screenshots from the games. Then the detection maps averaged over a number of observers are compared with results generated by IQMs. The obtained results show that SSIM and MS-SSIM metrics outperform other techniques. However, the results are not indisputable, because, for small and scattered aliasing artifacts, HDR-VDP-2 metrics report the results most consistent with the average human observer. As a proof of concept, we propose an application in which resolution of the shadow maps is controlled by the SSIM metric to avoid perceptually visible aliasing artifacts on the shadow edges.