论文标题

用关节介绍和预测来压缩颜色图像

Compressing Colour Images with Joint Inpainting and Prediction

论文作者

Mohideen, Rahul Mohideen Kaja, Peter, Pascal, Alt, Tobias, Weickert, Joachim, Scheer, Alexander

论文摘要

基于内部的编解码器将稀疏,量化的像素数据直接存储,并通过插值丢弃的图像零件来解码。通过预测要存储的像素数据,可以同时使用此插值来有效编码。这种关节介绍和预测方法通过简单的组件(例如常规网格和灰色价值图像上的Shepard插值)产生良好的结果,但它们缺乏用于颜色图像的专用模式。因此,我们评估了用于基于介入的颜色压缩的不同方法。介入的操作员能够从已知像素的小调色板中重建各种颜色。我们使用LUMA偏好模式来利用此功能,该模式在YCBCR颜色通道中使用的稀疏性比亮度通道中的稀疏性更高。此外,我们为基于内部的编解码器提出了第一个完整的矢量定量模式,该模式仅存储一个小的颜色代码。我们的实验表明,这两种颜色扩展都会取得重大改进。

Inpainting-based codecs store sparse, quantised pixel data directly and decode by interpolating the discarded image parts. This interpolation can be used simultaneously for efficient coding by predicting pixel data to be stored. Such joint inpainting and prediction approaches yield good results with simple components such as regular grids and Shepard interpolation on grey value images, but they lack a dedicated mode for colour images. Therefore, we evaluate different approaches for inpainting-based colour compression. Inpainting operators are able to reconstruct a large range of colours from a small colour palette of the known pixels. We exploit this with a luma preference mode which uses higher sparsity in YCbCr colour channels than in the brightness channel. Furthermore, we propose the first full vector quantisation mode for an inpainting-based codec that stores only a small codebook of colours. Our experiments reveal that both colour extensions yield significant improvements.

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