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Arbitrarily Shaped Motion Prediction for Depth Video Compression Using Arithmetic Edge Coding

机译:使用算术边缘编码进行深度视频压缩的任意形状运动预测

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Depth image compression is important for compact representation of 3D visual data in texture-plus-depth format, where texture and depth maps from one or more viewpoints are encoded and transmitted. A decoder can then synthesize a freely chosen virtual view via depth-image-based rendering using nearby coded texture and depth maps as reference. Further, depth information can be used in other image processing applications beyond view synthesis, such as object identification, segmentation, and so on. In this paper, we leverage on the observation that neighboring pixels of similar depth have similar motion to efficiently encode depth video. Specifically, we divide a depth block containing two zones of distinct values (e.g., foreground and background) into two arbitrarily shaped regions (sub-blocks) along the dividing boundary before performing separate motion prediction (MP). While such arbitrarily shaped sub-block MP can lead to very small prediction residuals (resulting in few bits required for residual coding), it incurs an overhead to transmit the dividing boundaries for sub-block identification at decoder. To minimize this overhead, we first devise a scheme called arithmetic edge coding (AEC) to efficiently code boundaries that divide blocks into sub-blocks. Specifically, we propose to incorporate the boundary geometrical correlation in an adaptive arithmetic coder in the form of a statistical model. Then, we propose two optimization procedures to further improve the edge coding performance of AEC for a given depth image. The first procedure operates within a code block, and allows lossy compression of the detected block boundary to lower the cost of AEC, with an option to augment boundary depth pixel values matching the new boundary, given the augmented pixels do not adversely affect synthesized view distortion. The second procedure operates across code blocks, and systematically identifies blocks along an object contour that should be coded using sub-block MP via- a rate-distortion optimized trellis. Experimental results show an average overall bitrate reduction of up to 33% over classical H.264/AVC.
机译:深度图像压缩对于以纹理加深度格式紧凑地表示3D可视数据非常重要,在该格式中,将编码和传输来自一个或多个视点的纹理和深度图。然后,解码器可以使用附近的编码纹理和深度图作为参考,通过基于深度图像的渲染来合成自由选择的虚拟视图。此外,深度信息可用于视图合成之外的其他图像处理应用程序中,例如对象识别,分割等。在本文中,我们利用观察到的相似深度的相邻像素具有相似的运动来有效地编码深度视频。具体而言,在执行单独的运动预测(MP)之前,我们沿着划分边界将包含两个具有不同值的区域(例如,前景和背景)的深度块划分为两个任意成形的区域(子块)。尽管这种任意形状的子块MP会导致非常小的预测残差(导致残差编码所需的比特数很少),但是在解码器处传输用于子块识别的划分边界却产生了开销。为了最大程度地减少这种开销,我们首先设计一种称为算术边缘编码(AEC)的方案来有效地编码将块划分为子块的边界。具体而言,我们建议以统计模型的形式将边界几何相关性纳入自适应算术编码器中。然后,我们提出了两种优化程序,以进一步提高给定深度图像的AEC边缘编码性能。第一个过程在代码块内操作,并允许对检测到的块边界进行有损压缩以降低AEC的成本,并且可以选择增加与新边界匹配的边界深度像素值,因为增强后的像素不会对合成视图失真产生不利影响。第二过程跨代码块进行操作,并沿着对象轮廓系统地识别应使用子块MP通过速率失真优化的网格进行编码的块。实验结果表明,与传统的H.264 / AVC相比,平均总比特率降低了33%。

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