Learning Disentangled Representations for Perceptual Point Cloud Quality Assessment via Mutual Information Minimization

TL;DR

This paper introduces DisPA, a novel framework for no-reference point cloud quality assessment that disentangles content and distortion representations using mutual information minimization, leading to improved accuracy.

Contribution

DisPA is the first to explicitly disentangle content and distortion features in NR-PCQA using a dual-branch network and mutual information minimization.

Findings

DisPA outperforms existing methods on multiple datasets.

Disentangled representations improve quality prediction accuracy.

The framework effectively separates semantic content from distortion details.

Abstract

No-Reference Point Cloud Quality Assessment (NR-PCQA) aims to objectively assess the human perceptual quality of point clouds without relying on pristine-quality point clouds for reference. It is becoming increasingly significant with the rapid advancement of immersive media applications such as virtual reality (VR) and augmented reality (AR). However, current NR-PCQA models attempt to indiscriminately learn point cloud content and distortion representations within a single network, overlooking their distinct contributions to quality information. To address this issue, we propose DisPA, a novel disentangled representation learning framework for NR-PCQA. The framework trains a dual-branch disentanglement network to minimize mutual information (MI) between representations of point cloud content and distortion. Specifically, to fully disentangle representations, the two branches adopt different philosophies: the content-aware encoder is pretrained by a masked auto-encoding strategy, which can allow the encoder to capture semantic information from rendered images of distorted point clouds; the distortion-aware encoder takes a mini-patch map as input, which forces the encoder to focus on low-level distortion patterns. Furthermore, we utilize an MI estimator to estimate the tight upper bound of the actual MI and further minimize it to achieve explicit representation disentanglement. Extensive experimental results demonstrate that DisPA outperforms state-of-the-art methods on multiple PCQA datasets.