通过优化多种特征类型，显著提升图像修复压缩的重建质量。
Optimizing Multiple Feature Types for Image Inpainting in the Linear and Nonlinear Setting
- 提出可融合线性与非线性特征的通用框架
- 特征种类从1增至5，平均提升2.76 dB（同存储量下）
- 自动选择最优特征位置与类型，适合图像压缩研究者
基于修复的压缩方法通过存储精心优化的图像子集并利用修复技术重建缺失数据。其压缩质量关键取决于所存储的数据。目前这些数据几乎仅包含像素位置及其灰度或颜色值。本文提出一个通用理论与实用框架，可引入由线性或非线性方程描述的任意特征，例如任意阶导数或局部积分。这些特征可与线性或非线性修复算子结合使用。此外，我们提出一种算法，能自动优化所选特征的位置与类型。该方法使基于修复的压缩成为更通用、灵活且强大的范式。实验表明，当特征类型从1种增至5种时，质量持续提升：在同存储量下，调和（均匀扩散）修复的平均峰值信噪比提升2.76 dB，边缘增强扩散修复提升1.82 dB。
原文摘要 · Abstract (English)
Inpainting-based compression stores a carefully optimized subset of the full image data and reconstructs the missing data by inpainting. The quality of these lossy codecs depends decisively on the stored data. So far, these data consist almost exclusively of pixel locations along with their grayscale or color values. In the present paper, we present a general theory and a practical framework that allows to incorporate arbitrary features which can be described by linear or nonlinear equations. This includes e.g. derivatives of arbitrary order or local integrals. Our features can be combined with linear or nonlinear inpainting operators. Moreover, we present an algorithm that automatically optimizes the location and the type of the selected feature. The approach of allowing different types of optimized features turns inpainting-based compression into a more general, versatile and powerful paradigm. Our experiments report a consistent quality gain when increasing the number of feature types from 1 to 5. With the same amount of stored data, the average peak signal-to-noise improvement is 2.76 dB for harmonic (homogeneous diffusion) inpainting, and 1.82 dB for edge-enhancing diffusion inpainting.
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