Light-field fluorescence microscopy (LFM) is a powerful elegant compact method for long-term high-speed imaging of complex biological systems, such as neuron activities and rapid movements of organelles. LFM experiments typically generate terabytes of image data and require a substantial amount of storage space. Some lossy compression algorithms have been proposed recently with good compression performance. However, since the specimen usually only tolerates low-power density illumination for long-term imaging with low phototoxicity, the image signal-to-noise ratio (SNR) is relatively low, which will cause the loss of some efficient position or intensity information using such lossy compression algorithms. Here, we propose a phase-space continuity-enhanced bzip2 (PC-bzip2) lossless compression method for LFM data as a high-efficiency and open-source tool that combines graphics processing unit-based fast entropy judgment and multicore-CPU-based high-speed lossless compression. Our proposed method achieves almost 10% compression ratio improvement while keeping the capability of high-speed compression, compared with the original bzip2. We evaluated our method on fluorescence beads data and fluorescence staining cells data with different SNRs. Moreover, by introducing temporal continuity, our method shows the superior compression ratio on time series data of zebrafish blood vessels.

自适应递归最小二乘滤波器具有预测准确、收敛速度快的特点，该滤波器被多种高光谱图像无损压缩方案作为重要组成部分。然而传统递归最小二乘滤波器无法快速找到每个谱带的最优预测长度，其压缩方案的性能有待提升。针对该问题，本文提出基于格型递归最小二乘滤波器组的高光谱图像压缩方案。首先，该方案使用单边高斯预测器对待测像素点做谱带内预测，去除图像的空间相关性。其次，采用格型滤波器组筛选出每个谱带的最优滤波器，获得预测误差。并根据格型滤波器组链式序列更新的特点，简化最优滤波器的筛选过程，大幅度降低计算复杂度。最后对预测误差做算术编码。以AVIRIS 2006高光谱图像为测试数据集，本文算法对16位校准图像、16位未校准图像的平均压缩结果分别为3.34 bits/pixel和5.61 bits/pixel。该算法在获得良好压缩结果的情况下，计算时间低于同类别的其余算法。