Learning based image quality assessment (IQA) models have obtained impressive performance with the help of reliable subjective quality labels, where mean opinion score (MOS) is the most popular choice. However, in view of the subjective bias of individual annotators, the labor-abundant MOS (LA-MOS) typically requires a large collection of opinion scores from multiple annotators for each image, which significantly increases the learning cost. In this paper, we aim to learn robust IQA models from low-cost MOS (LC-MOS), which only requires very few opinion scores or even a single opinion score for each image. More specifically, we consider the LC-MOS as the noisy observation of LA-MOS and enforce the IQA model learned from LC-MOS to approach the unbiased estimation of LA-MOS. In this way, we represent the subjective bias between LC-MOS and LA-MOS, and the model bias between IQA predictions learned from LC-MOS and LA-MOS (i.e., dual-bias) as two latent variables with unknown parameters. By means of the expectation-maximization based alternating optimization, we can jointly estimate the parameters of the dual-bias, which suppresses the misleading of LC-MOS via a gated dual-bias calibration (GDBC) module. To the best of our knowledge, this is the first exploration of robust IQA model learning from noisy low-cost labels. Theoretical analysis and extensive experiments on four popular IQA datasets show that the proposed method is robust toward different bias rates and annotation numbers and significantly outperforms the other learning based IQA models when only LC-MOS is available. Furthermore, we also achieve comparable performance with respect to the other models learned with LA-MOS.

翻译：基于学习的图像质量评估模型凭借可靠的主观质量标签获得了令人瞩目的性能，其中平均意见得分是最常用的指标。然而，考虑到个体标注者的主观偏置，通常需要为每张图像收集大量标注者的意见分数才能获得劳动密集型MOS，这显著增加了学习成本。本文旨在通过低成本MOS学习鲁棒的IQA模型，该模型每张图像仅需极少量意见分数甚至单个意见分数。具体而言，我们将LC-MOS视为LA-MOS的带噪观测值，强制使从LC-MOS学习的IQA模型逼近LA-MOS的无偏估计。通过这种方式，我们将LC-MOS与LA-MOS之间的主观偏置，以及从LC-MOS和LA-MOS学习的IQA预测之间的模型偏置（即双偏置）表示为具有未知参数的两个潜变量。借助基于期望最大化的交替优化，我们能够联合估计双偏置的参数，从而通过门控双偏置校准模块抑制LC-MOS的误导性信息。据我们所知，这是首个从带噪低成本标签中学习鲁棒IQA模型的探索。在四个主流IQA数据集上的理论分析与大量实验表明，当仅提供LC-MOS时，所提方法对不同偏置比率和标注数量具有鲁棒性，且显著优于其他基于学习的IQA模型。此外，我们与使用LA-MOS学习的其他模型相比也取得了相当的性能。