Adaptively tiled image mosaics utilizing measures of color and region entropy

Image mosaicing involves splitting an input image into a set of tiles, then replacing each tile with another image from a large dataset so that, when viewed from a distance, the resulting image resembles the original. We present a new approach for generating image mosaics using variable sized tiles made up from patches taken from photographs, paintings and texture images. This is difierent from previous work, where either simple regular tiling or adaptive tiling based on variations of RGB color was used. We propose an adaptive tiling theme by means of region entropy. In order to avoid the mismatch in roughness between the sub-image in the tile region of the input image and tile images in the dataset that may arise in the previous RGB color based image descriptors, we introduce the region entropy into the image descriptor to achieve better matching in both color and roughness. We also propose a new metric to measure the quality of the image mosaic which takes both the similarity and the mutual information between the generated mosaics and input images into account. The final mosaic images in this work are obtained by optimizing an objective function based on this metric.