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One major challenge in image compression is to efficiently represent and encode high-frequency structure components in images, such as edges, contours, and texture regions. To address this issue for lossy image compression, in our previous work, we proposed a scheme to learn local image structures and efficiently predict image data based on this structure information. In this work, we applied this structure learning and prediction scheme to lossless image compression and developed a lossless image encoder. Our extensive experimental results demonstrate that the lossless image encoder is competitive and even outperforms the state-of-the-art lossless image compression methods.