IEEE Trans Image Process. 2015 May;24(5):1573-86. doi: 10.1109/TIP.2015.2400817. Epub 2015 Feb 6.
In this paper, we propose a new geometry representation method for multiview image sets. Our approach relies on graphs to describe the multiview geometry information in a compact and controllable way. The links of the graph connect pixels in different images and describe the proximity between pixels in 3D space. These connections are dependent on the geometry of the scene and provide the right amount of information that is necessary for coding and reconstructing multiple views. Our multiview image representation is very compact and adapts the transmitted geometry information as a function of the complexity of the prediction performed at the decoder side. To achieve this, our graph-based representation (GBR) carefully selects the amount of geometry information needed before coding. This is in contrast with depth coding, which directly compresses with losses the original geometry signal, thus making it difficult to quantify the impact of coding errors on geometry-based interpolation. We present the principles of this GBR and we build an efficient coding algorithm to represent it. We compare our GBR approach to classical depth compression methods and compare their respective view synthesis qualities as a function of the compactness of the geometry description. We show that GBR can achieve significant gains in geometry coding rate over depth-based schemes operating at similar quality. Experimental results demonstrate the potential of this new representation.
在本文中，我们提出了一种新的多视角图像集的几何表示方法。我们的方法依赖于图来以紧凑和可控的方式描述多视角的几何信息。图的连接连接不同图像中的像素，并描述 3D 空间中像素之间的接近度。这些连接取决于场景的几何形状，并提供了编码和重建多个视图所需的适量信息。我们的多视角图像表示非常紧凑，并根据解码器侧执行的预测的复杂性自适应地传输几何信息。为了实现这一点，我们的基于图的表示（GBR）在编码之前仔细选择所需的几何信息量。这与深度编码形成对比，深度编码直接对原始几何信号进行有损压缩，因此很难量化编码错误对基于几何的插值的影响。我们介绍了这种 GBR 的原理，并构建了一种有效的编码算法来表示它。我们将我们的 GBR 方法与经典的深度压缩方法进行比较，并根据几何描述的紧凑性比较它们各自的视图合成质量。我们表明，GBR 可以在与基于深度的方案相似的质量水平上实现显著的几何编码率增益。实验结果证明了这种新表示的潜力。
