A fast content-dependent interpolation approach via adaptive filtering
Improving the subjective quality and reducing the computational complexity of interpolation algorithms are important issues in video and network signal processing. To this end, we propose a fast adaptive image interpolation algorithm that classifies pixels and uses different linear interpolation kernels that are adaptive to the class of a pixel. Pixels are classified into regions relevant to the perception of an image, either in a texture region, an edge region, or a smooth region. Image interpolation is performed with Neville filters, which can be efficiently implemented by a lifting scheme. Since linear interpolation tends to over-smooth pixels in edge regions and texture regions, we apply the Laplacian operator to enhance the pixels in those regions. The results of simulations show that the proposed algorithm not only reduces the computational complexity of the process, but also improves the visual quality of the interpolated images.