Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

翻译：盲图像质量评估（BIQA）因失真多样性和图像内容变化而仍具挑战性，这使跨尺度的失真模式复杂化，并加剧了BIQA回归问题的难度。然而，现有BIQA方法往往未考虑多尺度失真模式与图像内容，且关于通过学习策略提升回归模型性能的研究甚少。本文提出一种简单有效的渐进式多任务图像质量评估模型（PMT-IQA），该模型包含多尺度特征提取模块（MS）与渐进式多任务学习模块（PMT），旨在帮助模型学习复杂失真模式，并依据人类从易到难的学习规律优化回归问题。为验证所提PMT-IQA模型的有效性，我们在四个广泛使用的公开数据集上进行实验，结果表明PMT-IQA的性能优于对比方法，且MS与PMT模块均能提升模型性能。