Importance-Driven Controllable Texture Compaction

Example-based texture synthesis is a powerful tool to produce high-quality textures of arbitrary sizes essential in many computer graphic applications. The quality of the input texture is of vital importance to the success of example-based texture synthesis. In this paper, we propose a new texture compaction framework that adaptively synthesizes small texture samples by abstracting the patterns of given large textures according to an importance map. This map is computed from the original texture based on visual saliency to emphasize the important parts of the image that are to be reserved better during compaction. With such enhanced texture compaction, we can re-synthesize textures that maintain the feature patterns of the original textures better compared with previous inverse texture synthesis.