Blind Quality Enhancement for G-PCC Compressed Dynamic Point Clouds

TL;DR

This paper introduces a novel blind quality enhancement model for compressed dynamic point clouds that improves visual quality without prior knowledge of distortion levels, leveraging temporal dependencies and adaptive feature fusion.

Contribution

It presents the first blind quality enhancement approach for G-PCC compressed point clouds, jointly modeling features across unknown distortion levels with a novel architecture.

Findings

Achieved average PSNR improvements of over 0.4 dB across components.

Reduced BD-rate by approximately 20% for color components.

Effective enhancement without prior distortion level knowledge.

Abstract

Point cloud compression often introduces noticeable reconstruction artifacts, which makes quality enhancement necessary. Existing approaches typically assume prior knowledge of the distortion level and train multiple models with identical architectures, each designed for a specific distortion setting. This significantly limits their practical applicability in scenarios where the distortion level is unknown and computational resources are limited. To overcome these limitations, we propose the first blind quality enhancement (BQE) model for compressed dynamic point clouds. BQE enhances compressed point clouds under unknown distortion levels by exploiting temporal dependencies and jointly modeling feature similarity and differences across multiple distortion levels. It consists of a joint progressive feature extraction branch and an adaptive feature fusion branch. In the joint progressive feature extraction branch, consecutive reconstructed frames are first fed into a recoloring-based motion compensation module to generate temporally aligned virtual reference frames. These frames are then fused by a temporal correlation-guided cross-attention module and processed by a progressive feature extraction module to obtain hierarchical features at different distortion levels. In the adaptive feature fusion branch, the current reconstructed frame is input to a quality estimation module to predict a weighting distribution that guides the adaptive weighted fusion of these hierarchical features. When applied to the latest geometry-based point cloud compression (G-PCC) reference software, i.e., test model category13 version 28, BQE achieved average PSNR improvements of 0.535 dB, 0.403 dB, and 0.453 dB, with BD-rates of -17.4%, -20.5%, and -20.1% for the Luma, Cb, and Cr components, respectively.