论文标题
高精度深度图的端到端无损耗压缩以伪淹没为指导
End-to-end lossless compression of high precision depth maps guided by pseudo-residual
论文作者
论文摘要
作为代表空间信息的基本数据格式，深度图广泛用于信号处理和计算机视野字段。通过激光扫描仪或激光雷达等设备的快速开发，产生了大量的高精度深度图。因此，迫切需要在高精度深度图中探索一种具有更好压缩比的新压缩方法。利用广泛的深度学习环境，我们提出了一种基于端到端学习的无损压缩方法，用于高精度深度图。整个过程由两个子处理组成，称为深度图的预处理和对处理深度图的无损耗压缩。深度无损压缩网络由两个子网络组成，分别是命名为有损压缩网络和无损压缩网络。我们利用伪残基的概念指导剩余的分布产生，并避免引入上下文模型。我们的端到端无损压缩网络在工程编解码器上实现了竞争性能，并且计算成本较低。
As a fundamental data format representing spatial information, depth map is widely used in signal processing and computer vision fields. Massive amount of high precision depth maps are produced with the rapid development of equipment like laser scanner or LiDAR. Therefore, it is urgent to explore a new compression method with better compression ratio for high precision depth maps. Utilizing the wide spread deep learning environment, we propose an end-to-end learning-based lossless compression method for high precision depth maps. The whole process is comprised of two sub-processes, named pre-processing of depth maps and deep lossless compression of processed depth maps. The deep lossless compression network consists of two sub-networks, named lossy compression network and lossless compression network. We leverage the concept of pseudo-residual to guide the generation of distribution for residual and avoid introducing context models. Our end-to-end lossless compression network achieves competitive performance over engineered codecs and has low computational cost.
