Improved Depth Compression by Depth Downsampling Guided by Color Super-Pixel Refinement Segmentation
Improved Depth Compression by Depth Downsampling Guided by Color Super-Pixel Refinement Segmentation
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DOI：
10.1109/dcc.2018.00062
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
M. Georgiev;A. Gotchev
中科院分区：
文献类型：
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作者：
M. Georgiev;A. Gotchev
我们提出了一种改进的深度压缩方案，它依赖于深度抽取的超像素分割的对齐的颜色数据的指导。我们修改后者，以确保边界一致的分割细化水平。此外，我们的多模态正则化重建的修改。我们还解决了颜色和深度图之间可能的错位问题。这种不对准产生边缘离群值，其误导编码过程中的误差优化。我们提出了一个有效的编码方案，这种离群值在所谓的“yieldflow”协议。我们比较了我们的新的和改进的方法对一些国家的最先进的方法，并证明它表现良好，特别是在低比特率区域。
We propose an improved depth compression scheme which relies on depth decimation guided by super-pixel segmentation of the aligned color data. We modify the latter to ensure border congruency of segmenation refinement levels. Furthermore, a modification of our multi-modal regularized reconstruction is presented. We address also the problem of possible misalignments between color and depth maps. Such misalignments produce edge outliers which mislead the error optimization in the coding process. We propose an efficient encoding scheme of such outliers in so called “yieldflow” protocol. We compare our new and imporved method against a number of state-of-art approaches and demonstrate that it performs favorably especially in the low bit rate region.