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Intelligent software defined network based digital video stabilization system using frame transparency threshold pattern stabilization method

机译:智能软件采用帧透明度阈值图案稳定方法的基于网络的数字视频稳定系统

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摘要

Digital Video Stabilization (DVS) allows the acquisition of video sequences without disturbing jerkiness and removing unwanted camera motion. Many researchers attempt to improve the performance of Digital Video Stabilization but all of them not get perfect results. Therefore in this research work we introduce different DVS strategy that proposes to transfer camera veins to a Frame Transparency Threshold Pattern Stabilization (FTTPS) algorithm. The proposed FTTPS algorithm consists of four steps: (i) Frame Representation; (ii) Motion Estimation; (iii) Filtering and (iv) Compensation. The proposed FTTPS method also utilizes an adaptive criterion for filtering and validation of motion vectors moreover to register a Global Motion Vector (GMV) for each frame, the proposed DVS method satisfactorily assesses the think camera motion by moving an unwanted motion of characteristics. The proposed Frame Transparency Threshold Pattern Stabilization method is basically customized in Matlab programming language, the simulation of the proposed system validated through Inter-Frame Transformation Fidelity (ITF) and Peak to Signal Noise Ratio (PSNR) Values and which is more pragmatic and takes less chip asset and general 93.12% efficiency accomplished utilizing proposed Frame Transparency Threshold Pattern Stabilization system. The suggested digital video stabilization system can be combined with sensible and robot vision, and visual monitoring structures for various low cameras display quality updates.
机译:数字视频稳定(DVS)允许获取视频序列,而不会扰乱抽搐并消除不需要的相机运动。许多研究人员试图提高数字视频稳定的性能,但所有这些都没有得到完美的结果。因此,在本研究工作中,我们介绍了不同的DVS策略,提出将相机静脉传递到帧透明度阈值模式稳定(FTTPS)算法。所提出的FTTPS算法由四个步骤组成:(i)帧表示; (ii)运动估计; (iii)过滤和(iv)补偿。所提出的FTTP方法还利用了用于对运动矢量的过滤和验证的自适应标准来为每个帧注册全局运动矢量(GMV),所提出的DVS方法通过移动特性的不需要的运动来令人满意地评估思想相机运动。所提出的帧透明度阈值图案稳定方法基本上是以matlab编程语言定制的,模拟通过帧间变换保真度(ITF)和峰值验证的建议系统和峰值到信号噪声比(PSNR)值,并且更加务实,并且需要较少芯片资产和一般93.12%利用所提出的框架透明度阈值模式稳定系统实现的效率。建议的数字视频稳定系统可以与明智的和机器人视觉相结合,以及针对各种低摄像机显示质量更新的视觉监控结构。

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