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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2022.3152065
|2 doi
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|a DE-627
|b ger
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Wang, Taiyu
|e verfasserin
|4 aut
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| 245 |
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|a Learning-Based Rate Control for Video-Based Point Cloud Compression
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 09.03.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Due to limited transmission resources and storage capacity, efficient rate control is important in Video-based Point Cloud Compression (V-PCC). In this paper, we propose a learning-based rate control method to improve the rate-distortion (RD) performance of V-PCC. A low-latency synchronous rate control structure is designed to reduce the overhead of pre-coding. The basic unit (BU) parameters are predicted accurately based on our proposed CNN-LSTM neural network, instead of the online updating approach, which can be inaccurate due to low consistency between adjacent 2D frames in V-PCC. When determining the quantization parameters for the BU, a patch-based clipping method is proposed to avoid unnecessary clipping. This approach is able to improve the RD performance and subjective dynamic point cloud quality. Experiments show that our proposed rate control method outperforms present approaches
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|a Journal Article
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| 700 |
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|a Li, Fan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cosman, Pamela C
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 31(2022) vom: 23., Seite 2175-2189
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
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|g volume:31
|g year:2022
|g day:23
|g pages:2175-2189
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|u http://dx.doi.org/10.1109/TIP.2022.3152065
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|d 31
|j 2022
|b 23
|h 2175-2189
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