Recently, 3D Gaussian Splatting (3DGS) has emerged as a promising scene representation technique for novel view synthesis. However, the large number of Gaussians used to represent the 3D scene poses challenges for storage and transmission. Existing advanced compression methods focus on designing various context models for entropy modeling, but lack the exploration of prediction techniques. In this paper, we dig into feature correlations in the anchor-based Gaussian representation and propose two types of feature prediction techniques to further reduce the scene redundancy. Firstly, we observe that context features contain rich scene priors, which are also helpful for reconstruction but often ignored by previous methods. To this end, we design a Context-based Weighted Prediction module to adaptively aggregate the anchor feature and the context feature for rendering, which can reduce the storage costs in anchors. Secondly, a high degree of similarity is discovered between different feature channels. To utilize the cross-channel correlations, we propose a Cross-channel Residual Prediction module, which further reduces the bit cost for coding anchor features. Extensive experiments show that our method can further enhance compression performance while maintaining rendering quality compared to existing 3DGS compression methods. Our code is available at https://github.com/Pomelomm/FP-GS.

The rapid advancement of immersive visual applications has drawn significant attention to multi-view video compression. However, no end-to-end learning compression model is proposed for multi-view video sequences with six degrees of freedom. In this paper, we first propose an end-to-end model MVCNet to enhance multi-view video compression performance as shown in Fig. 1. MVCNet eliminates spatial and temporal redundancy in multi-view data effectively. In our methods, an efficient encoding structure is designed for compression, which utilizes spatial and temporal information among frames and views to improve compression performance. Furthermore, we propose a hybrid prediction module, which combines different prediction methods to provide satisfactory images and reduce the bit rate. Besides, we demonstrate a strategy of the fusion network to perform adaptive reconstruction.

Recently, 3D Gaussian Splatting (3D-GS) techniques have effectively driven the development of novel view synthesis due to the fast rendering speed and high-quality rendering. In this paper, we propose a compressed 3D Gaussian splatting model with residual rendering to enhance rendering quality and reduce storage. In our model, we design a residual rendering strategy to enrich the scene details, which leverages residual Gaussian points to optimize basic Gaussian points and supplement missing information. Additionally, an octree-based geometric compression model is introduced to compress geometric location.

This paper presents a learned video compression method in response to video compression track of the 6th Challenge on Learned Image Compression (CLIC), at DCC 2024. Specifically, we propose a unified contextual video compression framework (UCVC) for joint Pframe and B-frame coding. Each non-intra frame refers to two neighboring decoded frames, which can be either both from the past for P-frame compression, or one from the past and one from the future for B-frame compression. In training stage, the model parameters are jointly optimized with both P-frames and B-frames. Benefiting from the designs, the framework can support both P-frame and B-frame coding and achieve comparable compression efficiency with that specifically designed for P-frame or B-frame. As for challenge submission, we report the optimal compression efficiency by selecting appropriate frame types for each test sequence. Our team name is PKUSZ-LVC.

In neural video codecs, current state-of-the-art methods typically adopt multi-scale motion compensation to handle diverse motions. These methods estimate and compress either optical flow or deformable offsets to reduce inter-frame redundancy. However, flow-based methods often suffer from inaccurate motion estimation in complicated scenes. Deformable convolution-based methods are more robust but have a higher bit cost for motion coding. In this paper, we propose a hybrid context generation module, which combines the advantages of the above methods in an optimal way and achieves accurate compensation at a low bit cost. Specifically, considering the characteristics of features at different scales, we adopt flow-guided deformable compensation at largest-scale to produce accurate alignment in de-tailed regions. For smaller-scale features, we perform flow-based warping to save the bit cost for motion coding. Furthermore, we design a local-global context enhancement module to fully explore the local-global information of previous reconstructed signals. Experimental results demonstrate that our proposed Hybrid Local-Global Context learning (HLGC) method can significantly enhance the state-of-the-art methods on standard test datasets.