arXiv:2609.14129v1 Announce Type: cross Abstract: Ultra-High-Definition (UHD) video presents significant challenges for efficient storage and real-time decoding. Learning-based methods, such as Neura...
The paper introduces an observation‑Gram matrix that captures how each Gaussian in a 3D Gaussian Splatting model is viewed from training camera directions. This matrix serves as a distortion metric, enabling closed‑form degree reduction, Lagrangian rate‑distortion degree allocation, and matrix‑weighted vector quantisation. When applied to the Compressed3D framework, the metric improves PSNR by 0.49 dB before fine‑tuning and still yields a 0.32 dB gain at matched bitrate without any training images, while a training‑free stack built on the metric is 15% smaller than the image‑free GSICO at equal quality on Mip‑NeRF 360.
ClearGS is a method for 3D Gaussian Splatting from handheld videos that addresses uneven viewpoint coverage and mixed frame quality. It introduces Reliability‑aware View Allocation to assign graded supervision weights based on appearance reliability, degradation risk, and geometric utility, and weakly reactivates suppressed frames to maintain trajectory coverage. Additionally, Render‑Guided In‑Video Restoration uses a frozen no‑reference restoration expert and perceptual scoring to recover details lost to blur or distortion, followed by Full‑Trajectory Repair Consolidation to preserve early‑introduced details. On the GS2E and GSOTM datasets, ClearGS achieves state‑of‑the‑art performance with consistent CLIP‑IQA and MUSIQ gains and LPIPS reductions across most degradation settings, all without paired sharp supervision or matched clean references.
The paper introduces a non‑uniform quantisation scheme designed for 3D Gaussian Splatting (3DGS) models, addressing their high bitrate demands. By applying importance‑weighted quantisation and merging, the method adapts to the data distribution and removes post‑voxelisation redundancy. Experiments on benchmark datasets show state‑of‑the‑art compression performance, and the scheme is compatible with any point‑cloud representation, positioning it as a candidate for future MPEG 3DGS standardisation.
Video quality assessment (VQA) plays a critical role in optimizing video delivery systems. While numerous objective metrics have been proposed to approximate human perception, the perceived quality strongly depends on viewing conditions and display characteristics.
arXiv:2608. 02549v2 Announce Type: replace-cross Abstract: Efficient and perceptually meaningful quality assessment is a fundamental requirement for image and video processing, compression, and streaming systems.
arXiv:2609.34367v2 Announce Type: replace Abstract: Learned image codecs (LICs) achieve high reconstruction quality, but their decoding speed is often insufficient for immersive virtual reality (VR)....
arXiv:2609.23005v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) enables high-quality novel view synthesis but incurs high storage and transmission costs due to dense Gaussian primitives....
Gaussian Splatting has emerged as an effective representation for video, but existing methods rely on per-video optimization. This leads to slow encoding and limits generalization across videos.
arXiv:2608. 09594v1 Announce Type: cross Abstract: Recently, AI-driven video generation has attracted considerable attention.
Recently, AI-driven video generation has attracted considerable attention. This surge increases the demand for reliable video quality assessment (VQA) metrics to evaluate AI-generated content (AIGC) videos and guide model optimization.
3D Gaussian Splatting (3DGS) has emerged as an effective representation for novel view synthesis and 3D scene reconstruction, creating an increasing demand for reliable quality assessment. Unlike conventional image quality assessment (IQA), the quality of a 3DGS scene depends not only on the perceptual fidelity of rendered views, but also on scene-level factors such as spatial structure and cross-view consistency.