Point Cloud Geometry and Color Coding in a Learning-Based Ecosystem for JPEG Coding Standards

Despite its novelty, learning-based coding for images and point clouds is already outperforming some of the best long-standing conventional codecs. In addition to its rising compression performance, learning-based coding has opened new opportunities, notably the use of a single compressed domain representation to provide both high fidelity reconstructions for human visualization as well as effective performance for computer vision tasks, effectively unifying the visual language for man and machine. This paper proposes a new double learning-based static point cloud geometry and color coding solution, which targets point cloud component scalability, rate control flexibility at coding time, and a unified compressed domain representation. The proposed solution exploits the synergies between learning-based coding for images and point clouds, through the current JPEG PCC standard for geometry coding and the JPEG AI standard for image/color coding, establishing a learning-based ecosystem for JPEG coding standards. The proposed solution is able to overcome some design limitations of the current JPEG PCC Verification Model, and significantly improve its RD performance, becoming competitive with MPEG PCC standards.