论文标题
多尺度功能和基于平行变压器的图像质量评估
Multi-Scale Features and Parallel Transformers Based Image Quality Assessment
论文作者
论文摘要
随着多媒体含量的增加，与多媒体相关的扭曲的类型也在增加。图像质量评估的问题在PIPAL数据集中得到了很好的扩展，这仍然是为研究人员解决的开放问题。虽然，最近提出的变形金刚网络已经在文献中用于图像质量评估。同时，我们注意到多尺度功能提取已被证明是图像质量评估的有前途的方法。但是，到目前为止，变压器网络用于图像质量评估的方式缺乏多尺度特征提取的这些特性。我们以我们的方法利用了这一事实，并通过整合了这两种有希望的质量评估技术来提出新的体系结构。我们在包括PIPAL数据集在内的各种数据集上的实验表明，所提出的集成技术优于现有算法。该算法的源代码可在线获得：https：//github.com/komalpal9610/iqa
With the increase in multimedia content, the type of distortions associated with multimedia is also increasing. This problem of image quality assessment is expanded well in the PIPAL dataset, which is still an open problem to solve for researchers. Although, recently proposed transformers networks have already been used in the literature for image quality assessment. At the same time, we notice that multi-scale feature extraction has proven to be a promising approach for image quality assessment. However, the way transformer networks are used for image quality assessment until now lacks these properties of multi-scale feature extraction. We utilized this fact in our approach and proposed a new architecture by integrating these two promising quality assessment techniques of images. Our experimentation on various datasets, including the PIPAL dataset, demonstrates that the proposed integration technique outperforms existing algorithms. The source code of the proposed algorithm is available online: https://github.com/KomalPal9610/IQA
