In this paper, we propose an image quality transformer (IQT) that successfully applies a transformer architecture to a perceptual full-reference image quality assessment (IQA) task. Perceptual representation becomes more important in image quality assessment. In this context, we extract the perceptual feature representations from each of input images using a convolutional neural network (CNN) backbone. The extracted feature maps are fed into the transformer encoder and decoder in order to compare a reference and distorted images. Following an approach of the transformer-based vision models, we use extra learnable quality embedding and position embedding. The output of the transformer is passed to a prediction head in order to predict a final quality score. The experimental results show that our proposed model has an outstanding performance for the standard IQA datasets. For a large-scale IQA dataset containing output images of generative model, our model also shows the promising results. The proposed IQT was ranked first among 13 participants in the NTIRE 2021 perceptual image quality assessment challenge. Our work will be an opportunity to further expand the approach for the perceptual IQA task.

翻译：在本文中, 我们提出一个图像质量变压器( IQT), 成功地将变压器结构应用到感知全参考图像质量评估( IQA) 任务中。 在图像质量评估中, 感知性表示变得更加重要 。 在这方面, 我们使用进化神经网络( CNN) 的骨干, 从每个输入图像中提取感知性特征表示。 提取的地貌图被输入变压器编码器和解码器, 以便比较参考和扭曲的图像 。 按照以变压器为基础的视觉模型方法, 我们使用额外可学习的质量嵌入和位置嵌入。 变压器的输出被传送到一个预测头, 以预测最终的质量分数 。 实验结果显示, 我们提议的模型对于标准 IQA 数据集具有杰出的性能。 对于包含基因化模型输出图像的大型 IQA 数据集, 我们的模型也展示了有希望的结果 。 拟议的 IQT 被排在以变压仪 2021 视觉图像质量评估的13名参与者中, 。 我们的工作将是一个机会, 进一步扩展 任务 。