Attention-based Level-Of-Detail (LOD) managers downgrade the quality of areas that are expected to go unnoticed by an observer to economize on computational resources. The perceptibility of lowered visual fidelity is determined by the accuracy of the attention model that assigns quality levels. Most previous attention based LOD managers do not take into account saliency provoked by context, failing to provide consistently accurate attention predictions. In this work, we extend a recent high level saliency model with four additional components yielding more accurate predictions: an object-intrinsic factor accounting for canonical form of objects, an object-context factor for contextual isolation of objects, a feature uniqueness term that accounts for the number of salient features in an image, and a temporal context that generates recurring fixations for objects inconsistent with the context. We conduct a perceptual experiment to acquire the weighting factors to initialize our model. We design C-LOD, a LOD manager that maintains a constant frame rate on mobile devices by dynamically re-adjusting material quality on secondary visual features of non-attended objects. In a proof of concept study we establish that by incorporating C-LOD, complex effects such as parallax occlusion mapping usually omitted in mobile devices can now be employed, without overloading GPU capability and, at the same time, conserving battery power.