A Benchmark for Gaussian Splatting Compression and Quality Assessment Study
Gorde:
| Argitaratua izan da: | arXiv.org (Jul 19, 2024), p. n/a |
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| Egile nagusia: | |
| Beste egile batzuk: | , , , |
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Cornell University Library, arXiv.org
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| Gaiak: | |
| Sarrera elektronikoa: | Citation/Abstract Full text outside of ProQuest |
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| LEADER | 00000nab a2200000uu 4500 | ||
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| 001 | 3083264833 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3083264833 | ||
| 045 | 0 | |b d20240719 | |
| 100 | 1 | |a Yang, Qi | |
| 245 | 1 | |a A Benchmark for Gaussian Splatting Compression and Quality Assessment Study | |
| 260 | |b Cornell University Library, arXiv.org |c Jul 19, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a To fill the gap of traditional GS compression method, in this paper, we first propose a simple and effective GS data compression anchor called Graph-based GS Compression (GGSC). GGSC is inspired by graph signal processing theory and uses two branches to compress the primitive center and attributes. We split the whole GS sample via KDTree and clip the high-frequency components after the graph Fourier transform. Followed by quantization, G-PCC and adaptive arithmetic coding are used to compress the primitive center and attribute residual matrix to generate the bitrate file. GGSS is the first work to explore traditional GS compression, with advantages that can reveal the GS distortion characteristics corresponding to typical compression operation, such as high-frequency clipping and quantization. Second, based on GGSC, we create a GS Quality Assessment dataset (GSQA) with 120 samples. A subjective experiment is conducted in a laboratory environment to collect subjective scores after rendering GS into Processed Video Sequences (PVS). We analyze the characteristics of different GS distortions based on Mean Opinion Scores (MOS), demonstrating the sensitivity of different attributes distortion to visual quality. The GGSC code and the dataset, including GS samples, MOS, and PVS, are made publicly available at https://github.com/Qi-Yangsjtu/GGSC. | |
| 653 | |a Distortion | ||
| 653 | |a Quality assessment | ||
| 653 | |a Datasets | ||
| 653 | |a Arithmetic coding | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Data compression | ||
| 653 | |a Adaptive sampling | ||
| 653 | |a Video compression | ||
| 700 | 1 | |a Yang, Kaifa | |
| 700 | 1 | |a Xing, Yuke | |
| 700 | 1 | |a Xu, Yiling | |
| 700 | 1 | |a Zhu, Li | |
| 773 | 0 | |t arXiv.org |g (Jul 19, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3083264833/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2407.14197 |