Convolutional Neural Network-Based Occupancy Map Accuracy Improvement for Video-Based Point Cloud Compression
| Autor: | Li Li, Anique Akhtar, Shan Liu, Wei Jia, Zhu Li |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Pixel
business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Point cloud Process (computing) 02 engineering and technology Convolutional neural network Computer Science Applications Upsampling Compression (functional analysis) Signal Processing 0202 electrical engineering electronic engineering information engineering Media Technology 020201 artificial intelligence & image processing Computer vision Point (geometry) Artificial intelligence Electrical and Electronic Engineering Bitstream business |
| Zdroj: | IEEE Transactions on Multimedia. 24:2352-2365 |
| ISSN: | 1941-0077 1520-9210 |
| Popis: | In video-based point cloud compression (V-PCC), a dynamic point cloud is projected onto geometry and attribute videos patch by patch for compression. In addition to the geometry and attribute videos, an occupancy map video is compressed into a V-PCC bitstream to indicate whether a two-dimensional(2D) point in the projected geometry video corresponds to any point in three-dimensional (3D) space. The occupancy map video is usually downsampled before compression to obtain a tradeoff between the bitrate and the reconstructed point cloud quality. Due to the accuracy loss in the downsampling process, some noisy points are generated, which leads to severe objective and subjective quality degradation of the reconstructed point cloud. To improve the quality of the reconstructed point cloud, we propose using a convolutional neural network (CNN) to improve the accuracy of the occupancy map video. We mainly make the following contributions. First, we improve the accuracy of the occupancy map video by formulating the problem as a binary segmentation problem since the pixel values of the occupancy map video are either0or1. Second, in addition to the downsampled occupancy map video, we introduce are constructed geometry video as the other input of the CNNto provide more useful information in order to indicate the occupancy map video. To the best of our knowledge, this is the first learning-based work to improve the performance of V-PCC. Compared to state-of-the-art schemes, our proposed CNN-based approach achieves much more accurate occupancy map videos and significant bitrate savings. |
| Databáze: | OpenAIRE |
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