CLIP-PCQA: Exploring Subjective-Aligned Vision-Language Modeling for Point Cloud Quality Assessment

TL;DR

CLIP-PCQA introduces a novel vision-language model for point cloud quality assessment that aligns with human subjective evaluation by using descriptive quality labels and a retrieval-based approach, outperforming existing methods.

Contribution

It proposes a language-driven, retrieval-based point cloud quality assessment method leveraging CLIP, incorporating opinion score distribution for better subjective alignment.

Findings

Outperforms state-of-the-art PCQA methods

Utilizes descriptive quality labels for assessment

Incorporates opinion score distribution for improved accuracy

Abstract

In recent years, No-Reference Point Cloud Quality Assessment (NR-PCQA) research has achieved significant progress. However, existing methods mostly seek a direct mapping function from visual data to the Mean Opinion Score (MOS), which is contradictory to the mechanism of practical subjective evaluation. To address this, we propose a novel language-driven PCQA method named CLIP-PCQA. Considering that human beings prefer to describe visual quality using discrete quality descriptions (e.g., "excellent" and "poor") rather than specific scores, we adopt a retrieval-based mapping strategy to simulate the process of subjective assessment. More specifically, based on the philosophy of CLIP, we calculate the cosine similarity between the visual features and multiple textual features corresponding to different quality descriptions, in which process an effective contrastive loss and learnable prompts are introduced to enhance the feature extraction. Meanwhile, given the personal limitations and bias in subjective experiments, we further covert the feature similarities into probabilities and consider the Opinion Score Distribution (OSD) rather than a single MOS as the final target. Experimental results show that our CLIP-PCQA outperforms other State-Of-The-Art (SOTA) approaches.