PCQA: A Strong Baseline for AIGC Quality Assessment Based on Prompt Condition

TL;DR

This paper introduces PCQA, a novel framework for assessing AIGC quality by leveraging prompt conditions through a hybrid encoding method and feature blending, validated on image and video datasets.

Contribution

It presents a new prompt-based quality assessment framework using dual-source CLIP encoding and ensemble feature mixing, advancing AIGC evaluation methods.

Findings

Effective in evaluating image quality on AIGIQA-20K

Accurate video quality assessment on T2VQA-DB

Outperforms existing AIGC quality metrics

Abstract

The development of Large Language Models (LLM) and Diffusion Models brings the boom of Artificial Intelligence Generated Content (AIGC). It is essential to build an effective quality assessment framework to provide a quantifiable evaluation of different images or videos based on the AIGC technologies. The content generated by AIGC methods is driven by the crafted prompts. Therefore, it is intuitive that the prompts can also serve as the foundation of the AIGC quality assessment. This study proposes an effective AIGC quality assessment (QA) framework. First, we propose a hybrid prompt encoding method based on a dual-source CLIP (Contrastive Language-Image Pre-Training) text encoder to understand and respond to the prompt conditions. Second, we propose an ensemble-based feature mixer module to effectively blend the adapted prompt and vision features. The empirical study practices in two datasets: AIGIQA-20K (AI-Generated Image Quality Assessment database) and T2VQA-DB (Text-to-Video Quality Assessment DataBase), which validates the effectiveness of our proposed method: Prompt Condition Quality Assessment (PCQA). Our proposed simple and feasible framework may promote research development in the multimodal generation field.