Due to the enormous volume of point cloud data, transmitting and storing the data requires large bandwidth and storage space. It could be a critical bottleneck, especially in tasks such as autonomous driving. In this letter, we propose a novel point cloud compression algorithm based on clustering. The proposed scheme starts with a range image-based segmentation step, which segments the three-dimensional (3-D) range data into ground and main objects. Then, it introduces a novel prediction method according to the segmented regions' shape. This prediction method is inspired by the depth modeling modes used in 3-D high-efficiency video coding for depth map coding. Finally, the few prediction residual is efficiently compressed with several lossless or lossy data compression techniques. Experimental results show that the proposed algorithm can largely eliminate the spatial redundant information of the point cloud data. The lossless compression scheme reaches a compression ratio of nearly 5%, which means that the point cloud is compressed to 5% of its original size without any distance distortion. Compared with other methods, the proposed compression algorithm also shows better performance.

In this letter, we propose a novel coding architecture for LiDAR point cloud sequences based on clustering and prediction neural networks. LiDAR point clouds are structured, which provides an opportunity to convert the 3D data to a 2D array, represented as range images. Thus, we cast the LiDAR point clouds compression as a range images coding problem. Inspired by the high efficiency video coding (HEVC) algorithm, we design a novel coding architecture for the point cloud sequence. The scans are divided into two categories: intra-frames and inter-frames. For intra-frames, a cluster-based intra-prediction technique is utilized to remove the spatial redundancy. For inter-frames, we design a prediction network model using convolutional LSTM cells, which is capable of predicting future inter-frames according to the encoded intra-frames. Thus, the temporal redundancy can be removed. Experiments on the KITTI data set show that the proposed method achieves an impressive compression ratio, with 4.10% at millimeter precision. Compared with octree, Google Draco and MPEG TMC13 methods, our scheme also yields better performance in compression ratio.

Light detection and ranging (LiDAR) plays an indispensable role in autonomous driving technologies, such as localization, map building, navigation and object avoidance. However, due to the vast amount of data, transmission and storage could become an important bottleneck. In this article, we propose a novel compression architecture for multi-line LiDAR point cloud sequences based on clustering and convolutional long short-term memory (LSTM) networks. LiDAR point clouds are structured, which provides an opportunity to convert the 3D data to 2D array, represented as range images. Thus, we cast the 3D point clouds compression as a range image sequence compression problem. Inspired by the high efficiency video coding (HEVC) algorithm, we design a novel compression framework for LiDAR data that includes two main techniques: intra-prediction and inter-prediction. For intra-frames, inspired by the depth modeling modes (DMM) adopted in 3D-HEVC, we develop a clustering-based intra-prediction technique, which can utilize the spatial structure characteristics of point clouds to remove the spatial redundancy. For inter-frames, we design a prediction network model using convolutional LSTM cells. The network model is capable of predicting future inter-frames using the encoded intra-frames. As a result, temporal redundancy can be removed. Experiments on the KITTI dataset demonstrate that the proposed method achieves an impressive compression ratio (CR), with 4.10% at millimeter precision, which means the point clouds can compress to nearly 1/25 of their original size. Additionally, compared with the well-known octree, Google Draco, and MPEG TMC13 methods, our algorithm yields better performance in compression ratio.

With the development of video technology, a large amount of video data generated from video conferences, sports events, live broadcasts and network classes flows into our daily lives. However, ultra-high-definition video transmission is still a challenge due to the limited network bandwidth and instability, which further affects the quality of video service closely linked with consumer electronic video display. To address this challenge, we propose a deep-learned perceptual quality control approach, which can significantly improve the video quality and visual experience at the same bandwidth. The proposed scheme mainly involves saliency region extraction, perceptual-based bits allocation, and video enhancement. Firstly, we exploit a multi-scale deep convolutional network module to predict the static saliency map that semantically highlights the salient regions. Secondly, we develop a recurrent neural network model to extract the dynamic saliency regions. Finally, a three-level rate allocation scheme is developed based on the resulted saliency guidance, which is more reasonable by taking into account the visual characteristics of human eyes. Experimental results on a large dataset show that our method achieves an average gain of 1.5dB on the salient regions without introducing an extra bandwidth burden, which significantly improves the visual experience and paves the way to intelligent video communication.