基于结构化观测矩阵的低复杂度视频编码

摘要

Recently ,applications of low‐complexity video coding have gained wide interests .Compressive sensing (CS) can sample and compress signal simultaneously which can be used to design low‐complexity video coding .Structured measurement matrices based CS is proposed for video codec to handle the hard realization of random measurement matrices .Characteristic and construction of structured measurements matrices is explored and theory guarantees of fidelity reconstruction for different structure are analyzed . Numerical simulation results of CS video codec algorithm based on structured measurement matrices veri‐fy the theory as well as the promising potentials of low‐complexity video application field owing to the hardware friendly and fast computation of the matirices .%低复杂度视频编码越来越受到人们的关注。压缩感知理论具有同时采样和压缩信号的特点，可用于低复杂度视频编码设计。针对基于随机观测矩阵的传统压缩感知（Compressive sensing ，CS）理论很难实际应用这一问题，提出采用结构化观测矩阵的CS算法对视频编解码。探讨了结构化观测矩阵的特点和构造方法，分析了不同类型结构化观测矩阵实现信号精确重构的理论，设计了基于结构化观测矩阵的CS视频编解码算法。实验证明了所提算法的有效性，同时由于结构化观测矩阵高效、易于硬件实现，因此该算法在低复杂度视频场合具有良好的应用前景。