Texture is an important feature in images and has been widely used in many applications. Based on the classified textures, this book presents a novel learning- and texture-based approach to design more efficient image processing algorithms. For context-based arithmetic coding, the block- and texture-based training process is first applied to train the multiple-template (MT) from the most representative texture features. Based on the MT, we next present a texture- and MT-based arithmetic coding algorithm to compress error-diffused images. For predictive coding, to improve the least square approach, we present a texture-based training process to construct the multiple-window (MW) for various image contents. Based on the MW, the texture- and MW-based prediction scheme is presented to compress gray images. For inverse halftoning, based on the proposed variance gain-based decision tree, a texture-based training process is presented to construct a lookup tree-table which will be used in the reconstructing process. In the reconstructing process, we propose an edge-based refinement scheme to enhance the quality of the the reconstructed gray image.

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