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Inpainting intergrate with decomposition for image compression

机译:修复与分解集成以实现图像压缩

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摘要

In this paper, we propose an image compression framework that integrates image decomposition technique with image inpainting to exploit visual redundancy in color images. An original image is first decomposed into geometry and texture using nonlinear filter decomposition model. Then, the geometry and texture image are compressed with edge-based inpainting and the standard JPEG respectively. At the decoder side, the decoded texture image adds to geometry image which is decoded using structure propagation and texture synthesis. In this scheme, we study different decomposition models and choose the best decomposition model to get pure texture image. Moreover, robustness of texture image is fully taken advantage. Based on that, more regions can be dropped at the encoder side without sacrificing the image quality. Experimental results show that our method significantly outperforms standard JPEG and image compression with edge-based inpainting.
机译:在本文中,我们提出了一种将图像分解技术与图像修复相结合的图像压缩框架,以利用彩色图像中的视觉冗余。首先使用非线性滤波器分解模型将原始图像分解为几何形状和纹理。然后,分别使用基于边缘的修补和标准JPEG压缩几何图形和纹理图像。在解码器端,解码后的纹理图像会添加到几何图像,然后使用结构传播和纹理合成对其进行解码。在该方案中,我们研究了不同的分解模型,并选择最佳的分解模型以获得纯纹理图像。而且,充分利用了纹理图像的鲁棒性。基于此,可以在不牺牲图像质量的情况下在编码器侧放置更多区域。实验结果表明,我们的方法在基于边缘的修复上明显优于标准JPEG和图像压缩。

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