DR IQA Database V2

In practical media distribution systems, visual content usually undergoes multiple stages of quality degradation along the delivery chain, but the pristine source content is rarely available at most quality monitoring points along the chain to serve as a reference for quality assessment. As a result, full-reference (FR) and reduced-reference (RR) image quality assessment (IQA) methods are generally infeasible. Although no-reference (NR) methods are readily applicable, their performance is often not reliable. On the other hand, intermediate references of degraded quality are often available, e.g., at the input of video transcoders, but how to make the best use of them in proper ways has not been deeply investigated.This database is associated with a research project whose main goal is to make one of the first attempts to establish a new IQA paradigm named degraded-reference IQA (DR IQA). We initiate work on DR IQA by restricting ourselves to a two-stage distortion pipeline. Most IQA research projects rely on the availability of appropriate quality-annotated datasets. However, we find that only a few small-scale subject-rated datasets of multiply distorted images exist at the moment. These datasets contain a few hundreds of images and include the LIVE Multiply Distorted (LIVE MD), Multiply Distorted IVL (MD IVL), and LIVE Wild Compressed (LIVE WCmp) databases. Such small-scale data is not only insufficient to develop robust machine learning based IQA models, it is also not enough to perform multiple distortions behavior analysis, i.e., to study how multiple distortions behave in conjunction with each other when impacting visual content simultaneously. Surprisingly, such detailed analysis is lacking even for the case of two simultaneous distortions.We address the above-mentioned and other issues in our research project titled Degraded Reference Image Quality Assessment. As part of this project, we address the scarcity of data by constructing two large-scale datasets called DR IQA database Version 1 (V1) and DR IQA database Version 2 (V2). Each of these datasets contains 34 pristine reference (PR) images, 1,122 singly distorted degraded reference (DR) images, and 31,790 multiply distorted final distorted (FD) images, making them the largest datasets constructed in this particular area of IQA to-date. These datasets formed the basis of multiple distortion behavior analysis and DR IQA model development conducted in the above-mentioned project. We hope that the IQA research community will find them useful. Here we are releasing DR IQA database V2, while DR IQA database V1 has been separately released, also on IEEE DataPort. If you use this database in your research then please cite the following paper (Details about the DR IQA project can also be found in this paper):S. Athar and Z. Wang, "Degraded Reference Image Quality Assessment," Accepted for publication in IEEE Transactions on Image Processing, 2022.

在实际的媒体分发系统中，视觉内容在传输链中通常会经历多个阶段的质量退化，但原始源内容在传输链中的大多数质量监控点很少可用，无法作为质量评估的参照。因此，全参照（FR）和简化参照（RR）图像质量评估（IQA）方法通常不可行。尽管无参照（NR）方法易于应用，但它们的性能往往不够可靠。另一方面，中间质量参照物虽然常见，例如在视频转码器的输入端，但如何恰当地充分利用这些参照物尚未得到深入研究。本数据库与一项研究项目相关联，该项目的主要目标是首次尝试建立一种名为退化参照图像质量评估（DR IQA）的新IQA范式。我们通过限制自身于两阶段失真管道来启动DR IQA的研究工作。大多数IQA研究项目依赖于适当质量标注数据集的可用性。然而，我们发现目前仅存在少数小规模的倍数失真图像的主观评级数据集，这些数据集包含数百张图像，包括LIVE倍数失真（LIVE MD）、倍数失真IVL（MD IVL）和LIVE野生压缩（LIVE WCmp）数据库。此类小规模数据不仅不足以开发基于机器学习的鲁棒IQA模型，而且也不足以进行多种失真行为的分析，即研究多种失真在同时影响视觉内容时如何相互作用。令人惊讶的是，即使是两种同时发生的失真情况，这种详细分析也尚未存在。我们针对上述问题及其他问题，在名为“退化参照图像质量评估”的研究项目中进行了研究。作为此项目的一部分，我们通过构建两个大规模数据集来解决数据稀缺的问题，这两个数据集分别称为DR IQA数据库版本1（V1）和DR IQA数据库版本2（V2）。每个数据集包含34张原始参照（PR）图像、1,122张单次失真退化参照（DR）图像和31,790张多重失真最终失真（FD）图像，使它们成为迄今为止在该特定IQA领域中构建的最大数据集。这些数据集构成了上述项目中进行的多种失真行为分析和DR IQA模型开发的基础。我们希望IQA研究界会发现它们是有用的。在此，我们发布DR IQA数据库V2，而DR IQA数据库V1已单独发布，也在IEEE DataPort上发布。如果您在研究中使用此数据库，请引用以下论文（有关DR IQA项目的详细信息也可在此论文中找到）：S. Athar和Z. Wang，'退化参照图像质量评估'，已接受在IEEE Transactions on Image Processing上的发表，2022年。