This work proposes to reduce visibility data volume using a baseline-dependent lossy compression technique that preserves smearing at the edges of the field-of-view. We exploit the relation of the rank of a matrix and the fact that a low-rank approximation can describe the raw visibility data as a sum of basic components where each basic component corresponds to a specific Fourier component of the sky distribution. As such, the entire visibility data is represented as a collection of data matrices from baselines, instead of a single tensor. The proposed methods are formulated as follows: provided a large dataset of the entire visibility data; the first algorithm, named $simple~SVD$ projects the data into a regular sampling space of rank$-r$ data matrices. In this space, the data for all the baselines has the same rank, which makes the compression factor equal across all baselines. The second algorithm, named $BDSVD$ projects the data into an irregular sampling space of rank$-r_{pq}$ data matrices. The subscript $pq$ indicates that the rank of the data matrix varies across baselines $pq$, which makes the compression factor baseline-dependent. MeerKAT and the European Very Long Baseline Interferometry Network are used as reference telescopes to evaluate and compare the performance of the proposed methods against traditional methods, such as traditional averaging and baseline-dependent averaging (BDA). For the same spatial resolution threshold, both $simple~SVD$ and $BDSVD$ show effective compression by two-orders of magnitude higher than traditional averaging and BDA. At the same space-saving rate, there is no decrease in spatial resolution and there is a reduction in the noise variance in the data which improves the S/N to over $1.5$ dB at the edges of the field-of-view.

翻译：本文提出了一种基于基线依赖的有损压缩技术，通过保留视场边缘的模糊效应来降低可见度数据量。我们利用矩阵秩的相关性质，即低秩近似可将原始可见度数据表示为基本分量之和，其中每个基本分量对应天空分布的一个特定傅里叶分量。基于此，整个可见度数据被表示为来自各基线的数据矩阵集合，而非单一张量。所提方法如下：给定整个可见度数据集，第一种算法名为$simple~SVD$，将数据投影到秩为$-r$数据矩阵的规则采样空间中。在该空间中，所有基线的数据具有相同秩，使得各基线的压缩因子保持一致。第二种算法名为$BDSVD$，将数据投影到秩为$-r_{pq}$数据矩阵的不规则采样空间中。下标$pq$表示数据矩阵的秩随基线$pq$变化，从而使压缩因子具有基线依赖性。以MeerKAT和欧洲甚长基线干涉测量网作为参考望远镜，评估所提方法与传统方法（如传统平均法和基线依赖平均法BDA）的性能。在相同的空间分辨率阈值下，$simple~SVD$和$BDSVD$的压缩效率均比传统平均法和BDA高两个数量级。在相同空间节省率下，空间分辨率未见下降，且数据噪声方差降低，使视场边缘的信噪比提升超过$1.5$ dB。