Visibility of image artifacts depends on the viewing conditions, such as display brightness and distance to the display. However, most image and video quality metrics operate under the assumption of a single standard viewing condition, without considering luminance or distance to the display. To address this limitation, we isolate brightness and distance as the components impacting the visibility of artifacts and collect a new dataset for visually lossless image compression. The dataset includes images encoded with JPEG andWebP at the quality level that makes compression artifacts imperceptible to an average observer. The visibility thresholds are collected under two luminance conditions: 10 cd/m², simulating a dimmed mobile phone, and 220 cd/m², which is a typical peak luminance of modern computer displays; and two distance conditions: 30 and 60 pixels per visual degree. The dataset was used to evaluate existing image quality and visibility metrics in their ability to consider display brightness and its distance to viewer. We include two deep neural network architectures, proposed to control image compression for visually lossless coding in our experiments.