论文标题
使用Mumford-Shah卡通模型压缩分段光滑的图像
Compressing Piecewise Smooth Images with the Mumford-Shah Cartoon Model
论文作者
论文摘要
压缩分段光滑图像对于许多数据类型（例如3D视频中的深度图或光流场）以进行运动补偿很重要。专门依赖于该任务中明确存储的分段的专业编解码器，因为它们保留了平滑区域之间的不连续性。但是，当前的方法依赖于在能量最小化方面缺乏清晰解释的临时分割。作为一种补救措施，我们得出了一个通用区域，该区域合并了Mumford-Shah卡通模型的算法。它将细分适用于段内容的任意重建操作员。尽管它具有概念上的简单性，但我们的框架可以胜过以前的基于段的压缩方法，而BPG则最多可超过3 dB。
Compressing piecewise smooth images is important for many data types such as depth maps in 3D videos or optic flow fields for motion compensation. Specialised codecs that rely on explicitly stored segmentations excel in this task since they preserve discontinuities between smooth regions. However, current approaches rely on ad hoc segmentations that lack a clean interpretation in terms of energy minimisation. As a remedy, we derive a generic region merging algorithm from the Mumford-Shah cartoon model. It adapts the segmentation to arbitrary reconstruction operators for the segment content. In spite of its conceptual simplicity, our framework can outperform previous segment-based compression methods as well as BPG by up to 3 dB.
