Tile art image generation using parallel greedy algorithm on the GPU and its approximation with machine learning

Tile art image generation is one of the non-photorealistic rendering methods. The generated digital image resembles artistic representation given digital photos and illustrations. The first contribution of this paper is to propose a tile image generation based on the greedy approach. The greedy approach is based on the characteristic of the human visual system to optimize generated images. In addition, to shorten the computation time, we show the parallel algorithm and its GPU acceleration technique. We have implemented it on NVIDIA Tesla V100 GPU. The experimental result shows that the GPU implementation attains a speed-up factor of 318 and 16.19 over the sequential CPU implementation and the parallel multi-core CPU implementation with 160 threads, respectively. The second contribution of this paper is to propose an approximation method using machine learning with deep neural networks. After learning the network with the tile art images generated by the greedy approach as training dataset, it can generate tile art images that well-reproduce the original images with tile patterns. Moreover, we show an additional machine learning technique by repeating the forwarding computation for the generated tile art image as an input image. As a result, using this technique, we can generate a tile art images with clear shape of tiles.