轻量化点云几何编码模型，降低复杂度并小幅提升压缩效率。
Lightweight Super Resolution-enabled Coding Model for the JPEG Pleno Learning-based Point Cloud Coding Standard
- 在压缩域中引入超分辨率模型，减少潜在通道数。
- 模型参数减少约70%，同时平均压缩效率略有提升。
- 适合资源受限环境下的点云编码应用。
点云应用因提供丰富沉浸式体验而日益流行，但其数据量巨大（每对象常达数百万点），亟需高效编码方案。JPEG Pleno学习型点云编码标准作为首个面向静态点云的学习型编码标准，已建立基础框架，并在压缩性能上优于传统及学习型替代方案。本文提出一种新型轻量化点云几何编码模型，显著降低标准复杂度，有助于该标准在资源受限环境中的广泛应用，同时实现微小但稳定的压缩效率增益。该模型创新性地采用压缩域超分辨率机制，并大幅减少潜在通道数量。整体模型参数减少约70%，在JPEG Pleno点云编码数据集上实现了轻微的平均压缩性能提升。
原文摘要 · Abstract (English)
While point cloud-based applications are gaining traction due to their ability to provide rich and immersive experiences, they critically need efficient coding solutions due to the large volume of data involved, often many millions of points per object. The JPEG Pleno Learning-based Point Cloud Coding standard, as the first learning-based coding standard for static point clouds, has set a foundational framework with very competitive compression performance regarding the relevant conventional and learning-based alternative point cloud coding solutions. This paper proposes a novel lightweight point cloud geometry coding model that significantly reduces the complexity of the standard, which is essential for the broad adoption of this coding standard, particularly in resource-constrained environments, while simultaneously achieving small average compression efficiency benefits. The novel coding model is based on the pioneering adoption of a compressed domain approach for the super-resolution model, in addition to a major reduction of the number of latent channels. A reduction of approximately 70% in the total number of model parameters is achieved while simultaneously offering slight average compression performance gains for the JPEG Pleno Point Cloud coding dataset.
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