We address the problem of light field dimensionality reduction for compression. We describe a local low rank approximation method using a parametric disparity model. The local support of the approximation is defined by super-rays. A super-ray can be seen as a set of super-pixels that are coherent across all light field views. A dedicated super-ray construction method is first described that constrains the super-pixels forming a given super-ray to be all of the same shape and size, dealing with occlusions. This constraint is needed so that the super-rays can be used as supports of angular dimensionality reduction based on low rank matrix approximation. The light field low rank assumption depends on how much the views are correlated, i.e., on how well they can be aligned by disparity compensation. We first introduce a parametric model describing the local variations of disparity within each super-ray. We then consider two methods for estimating the model parameters. The first method simply fits the model on an input disparity map. We then introduce a disparity estimation method using a low rank prior. This method alternatively searches for the best parameters of the disparity model and of the low rank approximation. We assess the proposed disparity parametric model, first assuming that the disparity is constant within a super-ray, and second by considering an affine disparity model. We show that using the proposed disparity parametric model and estimation algorithm gives an alignment of super-pixels across views that favours the low rank approximation compared with using disparity estimated with classical computer vision methods. The low rank matrix approximation is computed on the disparity compensated super-rays using a singular value decomposition (SVD). A coding algorithm is then described for the different components of the proposed disparity-compensated low rank approximation. Experimental results show performance gains, with a rate saving going up to 92.61%, compared with the JPEG Pleno anchor, for real light fields captured by a Lytro Illum camera. The rate saving goes up to 37.72% with synthetic light fields. The approach is also shown to outperform an HEVC-based light field compression scheme.

中文翻译：

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具有参数视差模型的光场压缩局部低秩逼近

我们解决了压缩时光场维数减少的问题。我们描述了使用参数视差模型的局部低秩逼近方法。逼近的局部支持由超射线定义。可以将超射线视为在所有光场视图上都连贯的一组超像素。首先描述一种专用的超射线构造方法，该方法将形成给定超射线的超像素约束为所有相同的形状和大小，并处理遮挡。需要此约束，以便可以将超级射线用作基于低秩矩阵近似的角度降维的支持。光场低秩假设取决于视图之间的关联程度，即视差补偿可以将视图对齐的程度。我们首先引入一个参数模型，描述每个超射线内视差的局部变化。然后，我们考虑两种估计模型参数的方法。第一种方法只是将模型拟合到输入视差图上。然后，我们介绍使用低秩先验的视差估计方法。该方法可替代地搜索视差模型和低秩近似的最佳参数。我们评估提出的视差参数模型，首先假定视差在超射线内是恒定的，其次通过考虑仿射视差模型。我们显示，与使用经典计算机视觉方法估计的视差相比，使用建议的视差参数模型和估计算法可提供跨视图的超像素对齐，从而有利于低秩逼近。使用奇异值分解（SVD）在视差补偿后的超射线上计算出低秩矩阵近似。然后针对所提出的视差补偿的低秩近似的不同分量描述一种编码算法。实验结果表明，对于使用Lytro Illum摄像机捕获的真实光场，与JPEG Pleno锚点相比，性能提高了，节省率高达92.61％。在合成光场下，节省率高达37.72％。还显示了该方法优于基于HEVC的光场压缩方案。实验结果表明，对于使用Lytro Illum摄像机捕获的真实光场，与JPEG Pleno锚点相比，性能提高了，节省率高达92.61％。在合成光场下，节省率高达37.72％。还显示了该方法优于基于HEVC的光场压缩方案。实验结果表明，对于使用Lytro Illum摄像机捕获的真实光场，与JPEG Pleno锚点相比，性能提高了，节省率高达92.61％。在合成光场下，节省率高达37.72％。还显示了该方法优于基于HEVC的光场压缩方案。