As the demand for 3D point clouds grows, the data volume is growing dramatically. To tackle this challenge, the Moving Picture Expert Group (MPEG) is developing the enhanced geometry-based point cloud compression (Enhanced G-PCC) standard, which uses Region-Adaptive Hierarchical Transform (RAHT) for highly efficient attribute coding. However, since the geometry of the current frame and the reference frame is different, the octree structure between them does not match, which affects the performance of inter prediction. Therefore, we propose a virtual reference frame-based inter prediction method by aligning the geometry of the reference frame and the current frame. Specifically, the geometry of the virtual reference frame comes from the current frame, while its attribute information comes from the reference frame. Experimental results show that the proposed method can significantly increase the proportion of inter predicted RAHT coefficients and thus achieve average Bjøntegaard Delta Rates (BD-rates) of-6.3%,-8.9%, and-8.4% for the Luma, Cb, and Cr components, respectively, under the lossless geometry and lossy attribute coding condition, compared to the state-of-the-art Enhanced G-PCC reference software version 28 release candidate 2 (TMC13v28.0-rc2). For the coding condition of lossy geometry and lossy attribute, the corresponding BD-rates are-6.5%,-11.3%, and-7.7%, respectively.

In recent years, three-dimensional (3D) point clouds, which are applicable in various fields such as the metaverse and immersive communication, are attracting increasing attention. Under constrained storage and bandwidth conditions, efficient point cloud compression (PCC) plays a crucial role. To address these challenges, the Moving Picture Experts Group has been actively developing the geometry-based point cloud compression (G-PCC) standard and has recently proposed a test model for dynamic solid point clouds called Solid G-PCC. However, several issues still hinder the coding efficiency of attributes, such as inaccurate prediction, redundant coding bits, and accumulated distortion due to dependencies between frames. To tackle these challenges, we propose a layer-based rate-distortion optimized (RDO) attribute coding (L-RDOAC) method. This approach incorporates a layer-based RDO prediction (L-RDOP) to enhance prediction accuracy, a layer-based RDO quantization (L-RDOQ) to minimize redundant coding bits, and a layer-based RDO Wiener filter (L-RDOWF) to reduce distortion. Experimental results demonstrate that the coding efficiency of the proposed method significantly outperforms the state-of-the-art G-PCC reference software, as assessed through both objective and subjective evaluations. Specifically, compared to the state-of-the-art GeS-TM version 7.0, the proposed L-RDOAC achieves average Bjøntegaard-delta (BD) rates of -7.94%, -10.95%, and -8.16% for Luma, Cr, and Cb, respectively, under the C1 configuration (lossless geometry with lossy attributes), while under the C2 configuration (lossy geometry with lossy attributes), the average BD-rates are -7.88%, -8.09%, and -4.87%, respectively, when octree-based geometry coding is used.

Due to the huge data volume of three dimensional point clouds, efficient point cloud compression (PCC) is very important and challenging under limited storage and bandwidth conditions. The Moving Picture Experts Group (MPEG) is actively developing the geometry-based point cloud compression (G-PCC) standard and plan to release the second edition of G-PCC, namely Enhanced G-PCC. In image and video compression, chroma components are typically encoded at a lower resolution than luma, with minimal perceptual quality loss. However, chroma subsampling has not yet been explored in PCC. We investigate the characteristics of points at different level of details, and propose a chroma subsampling that can be embedded with the codec of Enhanced G-PCC. Experimental results show that the proposed method outperforms the state-of-the-art Enhanced G-PCC reference software version29.0 in terms of coding efficiency and time complexity. Due to the excellent performance, the proposed method has been adopted by the MPEG and will be integrated into the upcoming version of the reference software of Enhanced G-PCC.

Three-dimensional (3D) point clouds are becoming more and more popular for representing 3D objects and scenes. Due to limited network bandwidth, efficient compression of 3D point clouds is crucial. To tackle this challenge, the Moving Picture Experts Group (MPEG) is actively developing the Geometry-based Point Cloud Compression (G-PCC) standard, incorporating innovative methods to optimize compression, such as the Region-Adaptive Hierarchical Transform (RAHT) nestled within a layer-by-layer octree-tree structure. Nevertheless, a notable problem still exists in RAHT, i.e., the proportion of zero residuals in the last few RAHT layers leads to unnecessary bitrate consumption. To address this problem, we propose an adaptive skip coding method for RAHT, which adaptively determines whether to encode the residuals of the last several layers or not, thereby improving the coding efficiency. In addition, we propose a rate-distortion cost calculation method associated with an adaptive Lagrange multiplier. Experimental results demonstrate that the proposed method achieves average Bjøntegaard rate improvements of -3.50%, -5.56%, and -4.18% for the Luma, Cb, and Cr components, respectively, on dynamic point clouds, when compared with the state-of-the-art G-PCC reference software under the common test conditions recommended by MPEG.

Point clouds, which directly record the geometry and attributes of scenes or objects by a large number of points, are widely used in various applications such as virtual reality and immersive communication. However, due to the huge data volume and unstructured geometry, efficient compression of point clouds is very crucial. The Moving Picture Expert Group is establishing a geometry-based point cloud compression (G-PCC) standard for both static and dynamic point clouds in recent years. Although lossy compression of G-PCC can achieve a very high compression ratio, the reconstruction quality is relatively low, especially at low bitrates. To mitigate this problem, we propose a high efficiency Wiener filter that can be integrated into the encoder and decoder pipeline of G-PCC to improve the reconstruction quality as well as the rate-distortion performance for dynamic point clouds. Specifically, we first propose a basic Wiener filter, and then improve it by introducing coefficients inheritance and variance-based point classification for the Luma component. Besides, to reduce the complexity of the nearest neighbor search during the application of the Wiener filter, we also propose a Morton code-based fast nearest neighbor search algorithm for efficient calculation of filter coefficients. Experimental results demonstrate that the proposed method can achieve average Bjøntegaard delta rates of -6.1%, -7.3%, and -8.0% for Luma, Chroma Cb, and Chroma Cr components, respectively, under the condition of lossless-geometry-lossy-attributes configuration compared to the latest G-PCC encoding platform (i.e., geometry-based solid content test model version 7.0 release candidate 2) by consuming affordable computational complexity.

Link prediction aims at inferring missing links or predicting future ones based on the currently observed network. This topic is important for many applications such as social media, bioinformatics and recommendation systems. Most existing methods focus on homogeneous settings and consider only low-order pairwise relations while ignoring either the heterogeneity or high-order complex relations among different types of nodes, which tends to lead to a sub-optimal embedding result. This paper presents a method named Heterogeneous Hypergraph Variational Autoencoder (HeteHG-VAE) for link prediction in heterogeneous information networks (HINs). It first maps a conventional HIN to a heterogeneous hypergraph with a certain kind of semantics to capture both the high-order semantics and complex relations among nodes, while preserving the low-order pairwise topology information of the original HIN. Then, deep latent representations of nodes and hyperedges are learned by a Bayesian deep generative framework from the heterogeneous hypergraph in an unsupervised manner. Moreover, a hyperedge attention module is designed to learn the importance of different types of nodes in each hyperedge. The major merit of HeteHG-VAE lies in its ability of modeling multi-level relations in heterogeneous settings. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of the proposed method.

Hashing is an efficient method for nearest neighbor search in large-scale data space by embedding high-dimensional feature descriptors into a similarity preserving Hamming space with a low dimension. However, large-scale high-speed retrieval through binary code has a certain degree of reduction in retrieval accuracy compared to traditional retrieval methods. We have noticed that multi-view methods can well preserve the diverse characteristics of data. Therefore, we try to introduce the multi-view deep neural network into the hash learning field, and design an efficient and innovative retrieval model, which has achieved a significant improvement in retrieval performance. In this paper, we propose a supervised multi-view hash model which can enhance the multi-view information through neural networks. This is a completely new hash learning method that combines multi-view and deep learning methods. The proposed method utilizes an effective view stability evaluation method to actively explore the relationship among views, which will affect the optimization direction of the entire network. We have also designed a variety of multi-data fusion methods in the Hamming space to preserve the advantages of both convolution and multi-view. In order to avoid excessive computing resources on the enhancement procedure during retrieval, we set up a separate structure called memory network which participates in training together. The proposed method is systematically evaluated on the CIFAR-10, NUS-WIDE and MS-COCO datasets, and the results show that our method significantly outperforms the state-of-the-art single-view and multi-view hashing methods.

Satellite image classification is a challenging task if the input labels are not sufficiently accurate. The automatic cloud cover assessment (ACCA), for example, aims to classify the cloud covers of satellite images as alphabetical categories from A to E showing the escalating levels of clouds; however, those labels for training are often obtained by a subjective qualitative assessment, i.e., they may be not accurate. Therefore, this paper studies how to conduct ACCA under this circumstance. We propose a label smoothing approach and improve the accuracy around 3 percentage points (e.g., from 75.9% to 78.4% for ResNet network) without changing other network structures and parameters.

Over the past decade, increasing research efforts have been concentrated on emotion recognition from physiological signals due to their capability on emotion information representation. Existing works mainly focus on exploring the relationship between stimulus and subjects, while ignoring the effects of latent correlations among different subjects, which are important for personalized emotion recognition. To tackle this issue, we aim to conduct emotion recognition using multi-modal physiological signals through an edge-weighted hyper-graph neural network, in which complex relationship among subjects is formulated using hypergraph for each modality respectively. In our model, the differences in significance of influence which various samples leave on the classification can be better represented. The major contribution of this network lies in its concern that the associate strengths between various samples are different, which have different impact on the training result. The hyperedge between the vertices with closer correlation should be assigned a larger weight. Reversely, the looser relation, the minor weight. To evaluate the proposed method, experiments have been conducted on the DEAP dataset and ASCERTAIN dataset. Experimental results and comparison with state-of-the-art methods show that the proposed method can achieve better performance.

In recent years, graph/hypergraph-based deep learning methods have attracted much attention from researchers. These deep learning methods take graph/hypergraph structure as prior knowledge in the model. However, hidden and important relations are not directly represented in the inherent structure. To tackle this issue, we propose a dynamic hypergraph neural networks framework (DHGNN), which is composed of the stacked layers of two modules: dynamic hypergraph construction (DHG) and hypergrpah convolution (HGC). Considering initially constructed hypergraph is probably not a suitable representation for data, the DHG module dynamically updates hypergraph structure on each layer. Then hypergraph convolution is introduced to encode high-order data relations in a hypergraph structure. The HGC module includes two phases: vertex convolution and hyperedge convolution, which are designed to aggregate feature among vertices and hyperedges, respectively. We have evaluated our method on standard datasets, the Cora citation network and Microblog dataset. Our method outperforms state-of-the-art methods. More experiments are conducted to demonstrate the effectiveness and robustness of our method to diverse data distributions.

This paper studies a multi-Unmanned Aerial Vehicles (UAVs) -enabled wireless powered communication network (WPCN). We present a novel use of UAVs with energy harvesting module. Thus, UAVs are used as flying base stations to collecting data and charging ground IoT devices via radio frequency (RF). Due to energy constraint and limited coverage of single UAV, its applications are limited. So we investigate a Multi-UAVs solution to enhance system transmitting performance via UAVs' cooperation. In particular, we focus on finding a tradeoff solution to prolong devices lifetime and improve the average uplink throughput efficiency among all ground users in a given time. To tackle this problem, we first consider a relaxed problem in which IoT devices are distributed in different residential blocks. A Work-Gain algorithm is designed for the relaxed problem. The performances comparison are presented in numerical results. Our performance analysis and simulation results have demonstrated that the proposed approach can improve the uplink performance approximately by 20% compared to other methods in certain circumstances.