Exploring Content Fidelity Perceptibility via Quality Adversarial Learning

Academic Background

Image Quality Assessment (IQA) is a fundamental problem in the field of computer vision, aiming to evaluate the fidelity of visual content in images. IQA has significant applications in areas such as image compression and restoration. Traditional IQA methods are mainly divided into Full-Reference (FR-IQA) and No-Reference (NR-IQA) categories. FR-IQA assesses image quality by comparing the differences between a distorted image and a reference image, while NR-IQA evaluates image quality based solely on the distorted image itself, without any reference. Due to the lack of reference images, NR-IQA faces significant challenges in assessing content fidelity, making it difficult to distinguish between original content and distortions.

In recent years, deep learning-based NR-IQA methods have made significant progress, but two major issues remain: First, most NR-IQA methods lack the ability to perceive content fidelity, making it difficult to accurately distinguish between original content and distortions when assessing image quality. Second, NR-IQA models often perform poorly in downstream applications such as image enhancement and restoration, especially in the absence of reference images, where the model may generate images that appear high-quality but actually contain artifacts.

To address these issues, this paper proposes a Quality Adversarial Learning (QAL) framework for NR-IQA, aiming to dynamically adjust and optimize the image quality assessment process, thereby improving the model’s performance in terms of content fidelity and prediction accuracy.

Paper Source

This paper is co-authored by Mingliang Zhou, Wenhao Shen, Xuekai Wei, Jun Luo, Fan Jia, Xu Zhuang, and Weijia Jia, affiliated with the School of Computer Science at Chongqing University, the State Key Laboratory of Mechanical Transmissions at Chongqing University, and the BNU-UIC Institute of Artificial Intelligence and Future Networks at Beijing Normal University. The paper was accepted by the International Journal of Computer Vision on December 18, 2024, and officially published in 2025.

Research Content

Research Process

The research process of this paper is divided into the following steps:

1. Problem Definition and Background Knowledge:

   • First, the paper defines the concept of content fidelity, which refers to the similarity between a distorted image and its original image in terms of visual content. The assessment of content fidelity is crucial for accurately reflecting human perception of image quality.
   • Second, the paper proposes a Bayesian Least Squares (BLS) estimator to estimate the ideal reference image in the absence of a reference image.

2. Quality Adversarial Learning Framework:

   • The proposed quality adversarial learning framework consists of three modules: “quality prediction,” “generation,” and “re-prediction.” The framework optimizes the quality prediction model by generating adversarial samples, while simultaneously using these adversarial samples to improve the model’s prediction accuracy and content fidelity.
   • In the pre-training phase, an autoencoder architecture is used to learn feature representations of images, and the model’s robustness is enhanced by introducing 25 types of artificial noise.
   • In the quality adversarial learning phase, the model is optimized in two stages: the first stage generates adversarial samples by maximizing the predicted quality, and the second stage optimizes the quality prediction model by jointly training on the original distorted images and the generated adversarial samples.

3. Experimental Setup and Results:

   • Experiments were conducted on six benchmark IQA datasets, including LIVE, CSIQ, and KADID-10K. The results show that the proposed method outperforms existing NR-IQA methods on multiple datasets, particularly in terms of content fidelity and prediction accuracy.
   • Additionally, cross-dataset evaluation and image quality optimization experiments further validate the effectiveness and robustness of the proposed method.

Main Results

1. Importance of Content Fidelity:

   • The paper experimentally verifies the importance of content fidelity in NR-IQA, especially in no-reference scenarios, where the ability to perceive content fidelity directly affects the model’s prediction accuracy.
   • By introducing the quality adversarial learning framework, the paper successfully improves the model’s performance in terms of content fidelity, enabling it to more accurately reflect human perception of image quality.

2. Effectiveness of Quality Adversarial Learning:

   • The proposed quality adversarial learning framework significantly improves the model’s prediction accuracy and content fidelity by generating adversarial samples to optimize the quality prediction model.
   • Experimental results show that the proposed method outperforms existing NR-IQA methods on multiple benchmark datasets, particularly in image quality optimization tasks.

3. Application of Image Quality Optimization:

   • The paper experimentally verifies the effectiveness of using the quality prediction model as a loss function for image quality optimization. The results show that the proposed method effectively reduces the generation of artifacts in image restoration tasks, improving the visual quality of the images.

Conclusions and Significance

This paper proposes a Quality Adversarial Learning-based NR-IQA method, which significantly improves the model’s performance in no-reference image quality assessment by introducing a content fidelity perception mechanism and an adversarial learning framework. The main contributions of the paper include:

1. Content Fidelity Perception Mechanism: For the first time, the paper introduces a content fidelity perception mechanism in NR-IQA, optimizing the model by generating adversarial samples to more accurately reflect human perception of image quality.
2. Quality Adversarial Learning Framework: The proposed quality adversarial learning framework dynamically adjusts and optimizes the image quality assessment process, significantly improving the model’s prediction accuracy and content fidelity.
3. Application of Image Quality Optimization: The paper experimentally verifies the effectiveness of using the quality prediction model as a loss function for image quality optimization, providing new insights for practical applications such as image restoration and enhancement.

Research Highlights

1. Content Fidelity Perception: The paper introduces a content fidelity perception mechanism in NR-IQA for the first time, addressing the shortcomings of existing methods in terms of content fidelity.
2. Quality Adversarial Learning Framework: The proposed quality adversarial learning framework significantly improves the model’s prediction accuracy and content fidelity by generating adversarial samples to optimize the model.
3. Application of Image Quality Optimization: The paper experimentally verifies the effectiveness of using the quality prediction model as a loss function for image quality optimization, providing new insights for practical applications such as image restoration and enhancement.

Summary

This paper proposes a Quality Adversarial Learning-based NR-IQA method, which significantly improves the model’s performance in no-reference image quality assessment by introducing a content fidelity perception mechanism and an adversarial learning framework. Experimental results show that the proposed method outperforms existing NR-IQA methods on multiple benchmark datasets, particularly in image quality optimization tasks. The research provides new insights and methods for image quality assessment and optimization, with significant theoretical and practical value.