Lightweight Super Resolution-enabled Coding Model for the JPEG Pleno Learning-based Point Cloud Coding Standard

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.

💡 Research Summary

The paper addresses the growing demand for efficient compression of massive point‑cloud (PC) data, which underpins immersive applications such as virtual reality, autonomous driving, and cultural heritage preservation. JPEG Pleno’s Learning‑based Point Cloud Coding (JPEG PCC) standard, finalized in 2024, introduced a deep‑learning (DL) based geometry codec that outperforms conventional MPEG G‑PCC and V‑PCC in most scenarios. However, the standard’s architecture is highly complex: five separate coding networks (≈5.1 M parameters each) and two super‑resolution (SR) networks (≈7.3 M parameters each) are required to support sampling factors (SF) of 1, 2, and 4. This results in a total parameter count exceeding 40 M, imposing heavy memory and computational burdens, especially on resource‑constrained platforms.

The authors propose a lightweight SR‑Enabled PC Geometry Coding model (SR‑EPCC) that reduces overall model complexity by roughly 70 % while delivering modest gains in rate‑distortion (RD) performance. The key innovations are: (1) a compressed‑domain SR approach, where the SR network operates directly on the latent representation produced by the encoder rather than on the decompressed point cloud. This eliminates the need for a separate feature‑extraction step at the decoder, cutting both memory usage and processing latency. (2) A substantial reduction in latent channel width and a unified multi‑branch architecture that simultaneously handles SF = 1, 2, 4 within a single model. By shrinking the Inception‑Residual Blocks (IRBs) and sharing weights across branches, the authors achieve a parameter count of about 12 M for the entire codec (coding + SR), compared with the original >40 M.

Training proceeds in multiple stages: first the auto‑encoder (AE) for geometry coding is optimized, then the compressed‑domain SR module is fine‑tuned on the latent space, and finally joint optimization aligns the Top‑k binarization thresholds (k_C for coding, k_S for SR) with the standard’s bitstream syntax. The model retains full compliance with JPEG PCC’s normative specifications, ensuring interoperability.

Experimental evaluation uses the official JPEG PCC test set, covering a wide range of point‑cloud densities and geometries. Results show that SR‑EPCC achieves average PSNR improvements of 0.1–0.3 dB over the baseline while reducing encoder/decoder memory footprints by more than 50 % and decreasing runtime by roughly 30 %. The compression gains are modest but consistent across datasets, confirming that the compressed‑domain SR does not compromise quality despite the aggressive parameter reduction.

The authors argue that integrating SR‑EPCC as the “JPEG SR‑EPCC codec” into the standard would dramatically improve its adoption in real‑world scenarios, especially on mobile, embedded, and edge devices where the original model’s resource demands are prohibitive. Future work is outlined, including extending the approach to dynamic point clouds, adaptive branching for a broader range of sampling factors, and hardware‑friendly kernel designs to further accelerate inference. Overall, the paper delivers a compelling solution that balances the high compression performance of learning‑based PC coding with the practical constraints of deployment, marking a significant step toward widespread use of JPEG PCC.