图像质量评价Code和Dataset

最新推荐文章于 2025-06-11 22:10:43 发布

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最新推荐文章于 2025-06-11 22:10:43 发布

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分类专栏：图像质量评价文章标签：图像质量评价

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本文介绍了图像质量评估（IQA）的基本概念，包括全参考、减少参考和无参考三种方法。强调了在无法进行主观评估的情况下，客观IQA指标的重要性。文章详细阐述了不同类型的IQA问题，提供了评估IQA方法性能的标准和数据库，并列举了一些现代IQA指标的性能评价。

图像质量评价Code和Dataset

Research on Image Quality Assessment

Lin Zhang, School of Software Engineering, Tongji University

Lei Zhang, Dept. Computing, The Hong Kong Polytechnic University

Key words: image quality assessment, IQA, FSIM, FSIM_C, SSIM, VIF, MS-SSIM, IW-SSIM, IFC, PSNR, NQM, VSNR, SR_SIM, MAD, GSM, RFSIM

Introduction

Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. With the rapid proliferation of digital imaging and communication technologies, image quality assessment (IQA) has been becoming an important issue in numerous applications such as image acquisition, transmission, compression, restoration and enhancement, etc. Since the subjective IQA methods cannot be readily and routinely used for many scenarios, e.g. real-time and automated systems, it is necessary to develop objective IQA metrics to automatically and robustly measure the image quality. Meanwhile, it is anticipated that the evaluation results should be statistically consistent with those of the human observers. To this end, the scientific community has developed various IQA methods in the past decades. This website aims to provide enough basic knowledge of IQA to the beginners in this direction.

What are different categories of IQA problems?

According to the availability of a reference image, objective IQA metrics can be classified as full reference (FR), no-reference (NR) and reduced-reference (RR) methods.

Full Reference (FR) IQA In FR IQA problem, the distortion free image is given. Such an image usually is considered to have a "perfect" quality and is called "reference image" in IQA terminologies. Then, a set of its distorted versions are also provided. Your task is to present a computerized algorithm to evaluate the perceptual quality of each distorted image.

Reduced Reference (RR) IQA In RR IQA problem, the distorted image is of course given. The reference image is not available; however, partial information of the reference image is known. What kind of information is known of the reference image depends on your algorithm's scheme. Such RR algorithms can be useful in applications such as real-time visual information communications over wired or wireless networks, where they can be adopted to monitor image quality degradations or control the network streaming resources. The general framework explaining how an RR IQA metric works can be illustrated by the following figure, which is extracted from the paper "Q. Li, Z. Wang, Reduced-reference image quality assessment using divisive normalization-based image representation, IEEE Trans. Image Processing, vol. 3, no. 2, pp. 202-211, 2009".

No Reference (NR) IQA In NR IQA problem, only the distorted image is given. Or more accurately in such a case, we cannot call it as "distorted" image since we do not know the corresponding distortion-free reference image. You need to design an algorithm to evaluate the quality of the given image. NR is the most difficult IQA problems. To the best of my knowledge, there is no successful universal NR IQA algorithms currently. Most of the existing NR IQA metrics assume that the distortion affecting the image is known beforehand. For example, there exist NR IQA metrics to evaluate the quality of JPEG, JPEG2000, or blurred images.

How to evaluate IQA metrics?

If you want to evaluate the performance of existing IQA metrics or to compare your proposed IQA metric with the existed ones, you need to conduct experiments on the public available image databases established for the purpose of evaluating IQA metrics. Normally, in such