Predicting the quality of multimedia content is often needed in different fields. In some applications, quality metrics are crucial with a high impact, and can affect decision making such as diagnosis from medical multimedia. In this paper, we focus on such applications by proposing an efficient and shallow model for predicting the quality of medical images without reference from a small amount of annotated data. Our model is based on convolution self-attention that aims to model complex representation from relevant local characteristics of images, which itself slide over the image to interpolate the global quality score. We also apply domain adaptation learning in unsupervised and semi-supervised manner. The proposed model is evaluated through a dataset composed of several images and their corresponding subjective scores. The obtained results showed the efficiency of the proposed method, but also, the relevance of the applying domain adaptation to generalize over different multimedia domains regarding the downstream task of perceptual quality prediction. \footnote{Funded by the TIC-ART project, Regional fund (Region Centre-Val de Loire)}
翻译:在不同领域,往往需要预测多媒体内容的质量。在一些应用中,质量指标至关重要,影响很大,并可能影响决策,例如医学多媒体诊断。在本文件中,我们注重这些应用,方法是提出一个高效和浅度模型,用于预测医疗图像的质量,而无需参考少量附加说明的数据。我们的模型基于渐进式自我意识,目的是从相关当地图像特征中模拟复杂的代表形式,这些图像本身就滑过图像,以全球质量评分进行相互调试。我们还以不受监督和半监督的方式应用域适应学习。拟议模型通过由若干图像组成的数据集及其相应的主观分数进行评估。获得的结果表明拟议方法的效率,但也表明应用领域适应对不同多媒体领域关于概念质量预测的下游任务的相关性。\ footote{由东京-ART项目、区域基金(Region Centre-Val de Louire)资助。}