Real-time saliency-aware video abstraction

Existing real-time automatic video abstraction systems rely on local contrast only for identifying perceptually important information and abstract imagery by reducing contrast in low-contrast regions while artificially increasing contrast in higher contrast regions. These methods, however, may fail to accentuate an object against its background for the images with objects of low contrast over background of high contrast. To solve this problem, we propose a progressive abstraction method based on a region-of-interest function derived from an elaborate perception model. Visual contents in perceptually salient regions are emphasized, whereas the background is abstracted appropriately. In addition, the edge-preserving smoothing and line drawing algorithms in this paper are guided by a vector field which describes the flow of salient features of the input image. The whole pipeline can be executed automatically in real time on the GPU, without requiring any user intervention. Several experimental examples are shown to demonstrate the effectiveness of our approach.