多维高动态极光图像数据的混合预测无损压缩与非平稳信道传输研究

The aurora images acquired in the Polar Regions are characterized as multi-dimension, multi-scale and high dynamic gray levels in space, spectrum, time and multi-view, which results in huge-volume storage requirement. However, there are no effective compression and transmission methods available for aurora data so far, so the related study of the physics over the Polar Regions is seriously hysteretic. Traditional image compression methods ignore the multi-dimensional correlation in aurora data, so the lossless compression performance is poor. The motion compensation in video compression method is not suitable for the non-rigid target motion characteristic of aurora data and could not support super-dynamic lossless compression. By studying the auroral structure and physical mechanisms with different forms and non-rigid movement, the models of auroral movements will be built. By constructing the online adaptive training templates, high-order autoregression weighting based multi-dimensional and multi-scale hybrid prediction lossless compression method is studied. And the motion compensation coding for non-rigid target of aurora is presented. Also, the project will analyze the super-dynamic distribution of residual error and optimize the high-order entropy coding. Furthermore, the modeling of the non-stationary wireless channel and multivariate LDPC code based adaptive encoding algorithm are studied to improve the robustness of the transmission under wireless channel. Finally, we will establish an aurora compression coding and transmission system for the national Polar expedition. It is expected that the system can be used in the aurora observation system of the Polar Research Station to provide powerful supply for the Polar research and space weather research of our country.

我国极区科考观测的极光图像具有空间、谱间和时间的多维特性，不同视角的多尺度特点，形态的多样性和超高动态的灰度级，因此需要海量的存储空间。目前缺乏有效的极光图像压缩与传输方法，导致依赖于极光数据的极地高空物理相关研究严重滞后。传统的图像压缩方法忽略了极光数据的多维相关性，无损压缩性能极低；而视频压缩方法的运动补偿方法不适用于极光的非刚体运动特点，不支持超高动态的无损压缩。本项目通过研究不同形态、非刚体运动特征的极光结构和物理机理，建立极光运动模型，构造在线自适应训练模板，研究高阶自回归加权的多维和多尺度混合预测无损压缩方法；研究极光的非刚体运动补偿编码；分析残差的超高动态分布，优化高阶熵编码；研究非平稳无线信道建模与多元LDPC码的自适应编译码算法，提高传输的鲁棒性。完成我国极地科考极光观测的压缩编码和传输系统，并用于我国极地科考站的极光观测系统中，为我国极地科考与空间天气研究提供技术支撑。

我国极区科考观测的极光图像具有空间、谱间和时间的多维特性，不同视角的多尺度特点，形态的多样性和超高动态的灰度级，因此需要海量的存储空间。目前缺乏有效 的极光图像压缩与传输方法，导致依赖于极光数据的极地高空物理相关研究严重滞后。传统的图像压缩方法忽略了极光数据的多维相关性，无损压缩性能极低；而视频压缩方法的运动补偿方法不适用于极光的非刚体运动特点，不支持超高动态的无损压缩。本项目通过研究不同形态、非刚体运动特征的极光结构和物理机理，建立极光运动模型，构造在线自适应训练 模板，研究高阶自回归加权的多维和多尺度混合预测无损压缩方法；研究极光的非刚体运动补偿编码；分析残差的超高动态分布，优化高阶熵编码；研究非平稳无线信道建模的自适应编译码算法，提高传输的鲁棒性。完成我国极地科考极光观测的压缩编码和传输系统，并用于我国极地科考站的极光观测系统中，为我国极地科考与空间天气研究提供技术支撑。

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