Image Compression with Edge Local Variable Based Inpainting
Keywords:
Fuzzy Gibbs Random Field, local level processing, Multiresolution decomposition, Multispectral image fusionAbstract
In this thesis, image compression utilizing visual redundancy is investigated. Inspired by recent advancements in image inpainting techniques, we propose an image compression framework focused on visual quality with pixel-wise fidelity. In this work, an original image is analyzed at the encoder side so that parts of the image are intentionally and automatically skipped. Instead, some information is extracted from these skipped regions and delivered to the decoder as assistant information in a compressed manner. The delivered assistant information plays a key role in the proposed work because it provides guidelines for image inpainting with restoration, aiming to restore these regions accurately at the decoder side. Moreover, to fully take advantage of the assistant information, a compression-oriented edge-based inpainting restoration algorithm is proposed for image compression, integrating pixel-wise structure propagation and patch-wise texture synthesis. We also construct a practical system to verify the effectiveness of the compression approach, in which an edge map serves as assistant information, and the edge extraction and region removal approaches are developed accordingly. Our proposed method is a promising exploration for image and video compression.
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