arXiv Computer Vision
GS-PQM: A Parameter-Domain Quality Metric for Compressed Gaussian Splatting
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Deformable 2D Gaussian Splatting for Efficient 4K Video Compression
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...
arXiv Computer Vision
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Only What Was Seen: Observation-Gram Compaction of View-Dependent Appearance in 3D Gaussian Splatting
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.
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ClearGS: Reliability-Aware Gaussian Splatting from Handheld Videos
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.
arXiv Computer Vision
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Non-Uniform Quantisation for 3DGS Compression
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.
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Learning Flexible Generalization in Video Quality Assessment by Bringing Device and Viewing Condition Distributions
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 Computer Vision
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Estimating SSIM from MSE for DCT-Based Compressed Images via Modeling Local Error Statistics
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