chencn2020 / promptiqa

The public code for "PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Prompts"

PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Prompts

• The 18th European Conference on Computer Vision ECCV 2024

Zewen Chen, Haina Qin, Jun Wang, Chunfeng Yuan, Bing Li

National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (NLPR, CASIA).

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🚀 🚀 🚀 News:

• To be updated...
• ✅ March, 2023: We created this repository.

Online Demo[TBU]

Checklist

• [] Code for PromptIQA
• [] Code for training
• [] Code for testing
• [] Checkpoint
• [] Online Demo on huggingface

Catalogue

1. Introduction
2. Demonstrate
3. Usage For Training
4. Usage For Testing
5. Results
6. Citation
7. Acknowledgement

Introduction

This is an official implementation of PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Prompts by Pytorch.

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Due to the diversity of assessment requirements in various application scenarios for the IQA task, existing IQA methods struggle to directly adapt to these varied requirements after training. Thus, when facing new requirements, a typical approach is fine-tuning these models on datasets specifically created for those requirements. However, it is time-consuming to establish IQA datasets. In this work, we propose a Prompt-based IQA (PromptIQA) that can directly adapt to new requirements without fine-tuning after training. On one hand, it utilizes a short sequence of Image-Score Pairs (ISP) as prompts for targeted predictions, which significantly reduces the dependency on the data requirements. On the other hand, PromptIQA is trained on a mixed dataset with two proposed data augmentation strategies to learn diverse requirements, thus enabling it to effectively adapt to new requirements. Experiments indicate that the PromptIQA outperforms SOTA methods with higher performance and better generalization.

Figure1: The framework of the proposed PromptIQA.

Demonstrate

Usage For Training

Preparation

The dependencies for this work as follows:

TBU

You can also run the following command to install the environment directly:

TBU

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You can download the total 12 datasets from the following download link. (TBU)

Dataset	Task	Ref. Image	Dist. Types	Image Number	Label Type	Range	Download
LIVE	Synthetic Distortion Nature IQA	29	5	779	DMOS	[1, 100]
CSIQ		30	6	866	DMOS	[0, 1]
TID2013		25	24	3,000	MOS	[0, 9]
Kadid-10k		81	25	10,125	MOS	[1, 5]
BID	Authentic Distortion Nature IQA	-	-	586	MOS	[0, 5]	Link
SPAQ		-	-	11,125	MOS	[0, 100]	Link
LIVEC		-	-	1,162	MOS	[1, 100]	Link
KonIQ-10K		-	-	10,073	MOS	[0, 100]	Link
GFIQA20k	Face IQA	-	-	19,988	MOS	[0, 1]
AGIQA3k	AI generated IQA	-	-	2,982	MOS	[0, 1]
AIGCIQA2023		-	-	2,400	MOS	[0, 1]
UWIQA	Underwater IQA	-	-	890	MOS	[0, 1]

Training process

1. You should replace the dataset path in dataset_info.json to your own dataset path.
2. Run the following command to train the PromptIQA (Please review the train.py for more options).

TBU

Usage For Testing

Results

We achieved state-of-the-art performance on most IQA datasets simultaniously within one single model.

More detailed results can be found in the paper.

Individual Dataset Comparison.

Citation

If our work is useful to your research, we will be grateful for you to cite our paper:

Acknowledgement

We sincerely thank the great work HyperIQA, MANIQA and MoCo. The code structure is partly based on their open repositories.