The super-resolution (SR)-based coding tool is widely employed in modern video coding standards. By encoding video frames at a reduced resolution and then restoring them to their original resolution during the in-loop filtering stage, this tool helps to further reduce the bitrate and improve the coding performance. Current video coding standards typically devise rule-based SR methods in their codecs, compromising the coding efficiency to maintain low computational complexity. As deep neural network (DNN)-based SR methods are proving more effective than rule-based approaches, this paper proposes integrating the neural SR into video codecs to enhance coding performance while minimizing the computational cost. To this end, we propose a Lightweight Implicit Neural Model (LIM). Specifically, our LIM, consisting of Lightweight Feature Aggregation Network (LFANet) and Coordinate Upsampling Network Based on B-spline Representation (CURNet), is developed to support SR-based coding at an arbitrary scale. We exemplify the proposed method on the ongoing AVM reference software and conduct extensive experiments to demonstrate its effectiveness. Compared with anchored AVM, our method improves the BD-Rate by 5.52%, which significantly outperforms state-of-the-art works. Meanwhile, its computational complexity is much lower than others, having only 22.5k parameters and 18.7k FLOPs/pixel complexity, which is attractive to real-world applications.

This work extends the multiscale structure originally developed for point cloud geometry compression to point cloud attribute compression. To losslessly encode the attribute while maintaining a low bitrate, accurate probability prediction is critical. With this aim, we extensively exploit cross-scale, cross-group, and cross-color correlations of point cloud attribute to ensure accurate probability estimation and thus high coding efficiency. Specifically, we first generate multiscale attribute tensors through average pooling, by which, for any two consecutive scales, the decoded lower-scale attribute can be used to estimate the attribute probability in the current scale in one shot. Additionally, in each scale, we perform the probability estimation group-wisely following a predefined grouping pattern. In this way, both cross-scale and (same-scale) cross-group correlations are exploited jointly. Furthermore, cross-color redundancy is removed by allowing inter-color processing for YCoCg/RGB alike multi-channel attributes. The proposed method not only demonstrates state-of-the-art compression efficiency with significant performance gains over the latest G-PCC on various contents but also sustains low complexity with affordable encoding and decoding runtime.

Recent years have witnessed the growth of point cloud based applications for both immersive media as well as 3D sensing for auto-driving, because of its realistic and fine-grained representation of 3D objects and scenes. However, it is a challenging problem to compress sparse, unstructured, and high-precision 3D points for efficient communication. In this paper, leveraging the sparsity nature of the point cloud, we propose a multiscale end-to-end learning framework that hierarchically reconstructs the 3D Point Cloud Geometry (PCG) via progressive re-sampling. The framework is developed on top of a sparse convolution based autoencoder for point cloud compression and reconstruction. For the input PCG which has only the binary occupancy attribute, our framework translates it to a down-scaled point cloud at the bottleneck layer which possesses both geometry and associated feature attributes. Then, the geometric occupancy is losslessly compressed using an octree codec and the feature attributes are lossy compressed using a learned probabilistic context model. Compared with the state-of-the-art Video-based Point Cloud Compression (V-PCC) and Geometry-based PCC (G-PCC) schemes standardized by the Moving Picture Experts Group (MPEG), our method achieves more than 40% and 70% BD-Rate (BjØntegaard Delta Rate) reduction, respectively. We would like to make all materials publicly accessible at https://njuvision.github.io/PCGCv2/ for reproducible research.