Edge-directed geometric partitioning for versatile video coding
- URL: http://arxiv.org/abs/2606.01641v1
- Date: Mon, 01 Jun 2026 03:47:14 GMT
- Title: Edge-directed geometric partitioning for versatile video coding
- Abstract summary: We propose a GEO mode prediction strategy by constructing a Most Probable Mode (MPM) list to reduce the overhead of GEO and improve coding efficiency.<n>The proposed method provides an objective BD-rate gain of 0.58% and 1.00% on average for RA LDB configurations compared to VTM-6.0.
- Score: 57.010424923769385
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: To improve the coding performance, geometric partition (GEO) was proposed for the upcoming VVC standard. GEO provides 140 partition candidates. The index of optimal GEO mode needs to be signaled explicitly. Considering different structural characteristics of different CUs and the correlation between spatial adjacent blocks and temporal collocated blocks, we propose a GEO mode prediction strategy by constructing a Most Probable Mode (MPM) list to reduce the overhead of GEO index and improve coding efficiency. Based on the observation of the high correlation between the partition mode and object boundaries, an edge-directed geometric partition scheme is proposed to construct the MPM list according to spatio-temporal edge information. The proposed method provides an objective BD-rate gain of 0.58% and 1.00% on average for RA and LDB configurations compared to VTM-6.0. Besides, it also promotes the visual quality of object boundaries.
Related papers
- Spatio-Temporal Correlation Guided Geometric Partitioning for Versatile Video Coding [55.405068056883614]
We propose guided geometric partitioning (STGEO) to describe the object information signaling in the motion field of video coding.<n>The proposed method can economize the bits consumed for side information, including partitioning mode and motion information.<n>The main idea is to predict the STGEO modes and motion candidates that have higher selection possibilities.
arXiv Detail & Related papers (2026-06-01T05:11:50Z) - MCGI: Manifold-Consistent Graph Indexing for Billion-Scale Disk-Resident Vector Search [0.9179857807576733]
MCGI is a geometry-aware and disk-resident indexing method that adapts search strategies to the data's intrinsic geometry.<n> Empirically, MCGI achieves 5.8$times$ higher throughput at 95% recall on high-dimensional GIST1M compared to state-of-the-art DiskANN.<n>On the billion-scale SIFT1B dataset, MCGI further validates its scalability by reducing high-recall query latency by 3$times$, while maintaining performance parity on standard lower-dimensional datasets.
arXiv Detail & Related papers (2026-01-05T09:23:48Z) - A Gradient Meta-Learning Joint Optimization for Beamforming and Antenna Position in Pinching-Antenna Systems [63.213207442368294]
We consider a novel optimization design for multi-waveguide pinching-antenna systems.<n>The proposed GML-JO algorithm is robust to different choices and better performance compared with the existing optimization methods.
arXiv Detail & Related papers (2025-06-14T17:35:27Z) - Object Segmentation-Assisted Inter Prediction for Versatile Video Coding [53.91821712591901]
We propose an object segmentation-assisted inter prediction method (SAIP), where objects in the reference frames are segmented by some advanced technologies.
With a proper indication, the object segmentation mask is translated from the reference frame to the current frame as the arbitrary-shaped partition of different regions.
We show that the proposed method achieves up to 1.98%, 1.14%, 0.79%, and on average 0.82%, 0.49%, 0.37% BD-rate reduction for common test sequences.
arXiv Detail & Related papers (2024-03-18T11:48:20Z) - Floorplanning of VLSI by Mixed-Variable Optimization [42.82770651937298]
This paper proposes memetic algorithms to solve mixed-variable floorplanning problems.
Proposed algorithms are superior to some celebrated B*-tree based floorplanning algorithms.
arXiv Detail & Related papers (2024-01-27T06:34:16Z) - Adaptive Spot-Guided Transformer for Consistent Local Feature Matching [64.30749838423922]
We propose Adaptive Spot-Guided Transformer (ASTR) for local feature matching.
ASTR models the local consistency and scale variations in a unified coarse-to-fine architecture.
arXiv Detail & Related papers (2023-03-29T12:28:01Z) - Robust Training in High Dimensions via Block Coordinate Geometric Median
Descent [69.47594803719333]
Geometric median (textGm) is a classical method in statistics for achieving a robust estimation of the uncorrupted data.
In this paper, we show that by that by applying textscGm to only a chosen block of coordinates at a time, one can retain a breakdown point of 0.5 judiciously for smooth nontext problems.
arXiv Detail & Related papers (2021-06-16T15:55:50Z) - Distributed Learning and Democratic Embeddings: Polynomial-Time Source
Coding Schemes Can Achieve Minimax Lower Bounds for Distributed Gradient
Descent under Communication Constraints [46.17631511884969]
We consider the problem of compressing a vector in the n-dimensional Euclidean space, subject to a bit-budget of R-bits per dimension.
We show that Democratic and Near-Democratic source-coding schemes are (near) optimal in the sense that the covering efficiency of the resulting quantizer is either dimension independent, or has a very weak logarithmic dependence.
We propose a distributed optimization algorithm: DGD-DEF, which employs our proposed coding strategy, and achieves the minimax optimal convergence rate to within (near) constant factors.
arXiv Detail & Related papers (2021-03-13T00:04:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.