基于自适应采样的多假设预测残差重构算法研究

Research on Multi-hypothesis Residual Reconstruction Algorithm Based on Adaptive Sampling

为保证遥感视频序列的高质量重构,本文结合视频序列的高时空冗余特点,在基于块的分布式视频压缩感知(Distributed video compressed sensing,DVCS)框架的基础上提出了一种基于自适应采样的多假设预测残差重构模型及基于变采样率的多假设预测残差重构算法.首先对目标帧进行预测,根据各块预测精度的不同自适应地分配采样率;然后用变采样率多假设预测残差重构算法重构出目标帧;最后利用双向运动估计对重构结果进行修正.仿真结果表明该算法能够在降低采样率的同时保证良好的主客观重构质量;相同采样率条件下,重构精度比MC-BCS-SPL算法提高大约7 d B,比MH-BCS-SPL算法提高大约1 d B.

To guarantee an adequate quality reconstruction of remote sensing video and to make use of the character of mass information redundancy in video, this paper proposes a kind of adaptive sampling and multi-hypothesis residual reconstruction model and algorithm based on the framework called distributed video compressed sensing （DVCS）. Firstly, the current frame is predicted, then sampling rates are adaptively Mlocated according to the precise degrees of the blocks in the predicted frame. Afterwards, the current frame is reconstructed using the variable sampling rates multi-hypothesis residual reconstruction algorithm. Finally, the reconstructed frame is revised by bilateral motion estimation tectmic. Simulation shows that the proposed model and algorithm can assure low sampling rate and high reconstruction quality simultaneously, and that the model can offer a PSNR gain of around 7 dB higher the MC-BCS-SPL algorithm, and a gain of around 1 dB than higher the MH-BCS-SPL algorithm when the sampling rates are same.