Current video-based GS compression methods rely on using Parallel Linear Assignment Sorting (PLAS) to convert 3D GS into smooth 2D maps, which are computationally expensive and time-consuming, limiting the application of GS on lightweight devices. In this paper, we propose a Lightweight 3D Gaussian Splatting (GS) Compression method based on Video codec (LGSCV). First, a two-stage Morton scan is proposed to generate blockwise 2D maps that are friendly for canonical video codecs in which the coding units (CU) are square blocks. A 3D Morton scan is used to permute GS primitives, followed by a 2D Morton scan to map the ordered GS primitives to 2D maps in a blockwise style. However, although the blockwise 2D maps report close performance to the PLAS map in high-bitrate regions, they show a quality collapse at medium-to-low bitrates. Therefore, a principal component analysis (PCA) is used to reduce the dimensionality of spherical harmonics (SH), and a MiniPLAS, which is flexible and fast, is designed to permute the primitives within certain block sizes. Incorporating SH PCA and MiniPLAS leads to a significant gain in rate-distortion (RD) performance, especially at medium and low bitrates. MiniPLAS can also guide the setting of the codec CU size configuration and significantly reduce encoding time. Experimental results on the MPEG dataset demonstrate that the proposed LGSCV achieves over 20% RD gain compared with state-of-the-art methods, while reducing 2D map generation time to approximately 1 second and cutting encoding time by 50%. The code is available at https://github.com/Qi-Yangsjtu/LGSCV.

To address the mesh quality assessment (MQA) problem, GeodesicP-SIM was proposed by jointly considering geometry and color features, demonstrating compelling performance in multiple benchmarks.However, GeodesicPSIM does not consider the multi-scale characteristics of human perception.To better mimic human subjective perception, we proposed a multi-scale MQA model called multi-scale Geodesic Patch Similarity (MS-GeodesicPSIM).Firstly, inspired by the multi-scale processing methods used in image and point cloud analysis, we propose a novel multi-scale representation of textured meshes based on mesh simplification techniques.Secondly, we extend GeodesicPSIM into a multi-scale version leveraging the proposed multi-scale representation.Specifically, we construct a multi-scale representation for the reference and distorted meshes, followed by fusing the results of GeodesicPSIM at different scales to obtain an overall quality score.Experimental results demonstrate the superior performance of the proposed MS-GeodesicPSIM compared to the single-scale GeodesicPSIM and other MQA metrics on three large and independent databases.Ablation studies further confirm that MS-GeodesicPSIM is robust to different model hyperparameter settings.The code for MS-GeodesicPSIM is available at https://github.com/ccccby/MS-GeodesicPSIM