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Compression scheme by use of object-segmented sub-image array transformed from computational elemental image array based on multiple objects in 3D integral imaging

机译:通过在3D积分成像中使用基于多个对象的计算元素图像阵列转换的对象分段子图像阵列进行压缩的方案

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In this paper, we address a highly enhanced compression scheme in the condition of multiple objects in IntegralImaging (InIm) by use of sub-images (SIs) to segment each object and to remove the Motion Vector (MV) of residual image array transformed from Sub-Image Array (SIA). In the pick-up process, SIA is generated from EIA after the perspectives passing through virtual pinhole array is recorded as Elemental Image Array (EIA). The similarity enhancement among SIs expects compression efficiency to improve, but the compression efficiency of the EIA in the picked-up condition of multiple objects does not correspond to that of the picked-up condition of a simplified object. In the proposed scheme, the depth of objects is computed by two adaptive SIs located at horizontal left and right side from the reference SI positioned to the center of the SIA. A depth map image generated from two adaptive the SIs and a reference SI is applied to segment each object considering to the distance between those. Therefore, an adaptive objectsegmented SI is obtained and, which is motion-estimated from the original SIA based on MSE to generate the motioncompensated object-segmented SIA and which SIAs from each segmented object are finally combined as the motioncompensated SIA, and which based on multiple objects is transformed to residual SIA to minimize the spatial redundancy and which SIA is compressed by MPEG-4. The proposed algorithm shows the enhanced compression efficiency than that of the baseline JPEG and the conventional EIA compression scheme.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
机译:在本文中,我们通过使用子图像(SI)分割每个对象并去除从中转换而来的残差图像阵列的运动矢量(MV),解决了IntegralImaging(InIm)中多个对象情况下的高度增强的压缩方案。子图像阵列(SIA)。在拾取过程中,通过虚拟针孔阵列的视点记录为元素图像阵列(EIA)后,从EIA生成SIA。 SI之间的相似性增强期望压缩效率提高,但是EIA在多个物体的拾取条件下的压缩效率与简化物体的拾取条件不对应。在所提出的方案中,对象的深度是通过位于自SIA中心的参考SI位置位于水平左右两侧的两个自适应SI来计算的。从两个自适应SI和参考SI生成的深度图图像将考虑到每个对象之间的距离而应用于每个对象。因此,获得了一种自适应的分段对象的SI,并基于MSE从原始SIA进行运动估计,以生成经运动补偿的对象分段的SIA,并且最终将每个分割对象中的SIA组合为经运动补偿的SIA,并且基于多个将对象转换为残差SIA,以最大程度地减少空间冗余,并通过MPEG-4压缩哪个SIA。所提出的算法显示出比基线JPEG和传统EIA压缩方案更高的压缩效率。©(2012)COPYRIGHT光电仪器工程师协会(SPIE)。摘要的下载仅允许个人使用。

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