Recently, deep-learning-based blind picture quality measurement (BPQM) metrics have gained significant attention. However, training a robust deep BPQM metric remains a difficult and challenging task because of the limited number of subject-rated training samples. State-of-the-art full-reference (FR) picture quality measurement (PQM) metrics are in good agreement with human subjective quality scores. Therefore, they can be employed to approximate human subjective quality scores to train BPQM metrics. Inspired by this, we propose a deep encoder–decoder architecture (DEDA) for opinion-unaware (OU) BPQM that does not require human-labeled distorted samples for training. In the training procedure, to avoid overfitting and to ensure the independency of the training and testing samples, we first construct 6,000 distorted pictures and use their objective quality/similarity maps obtained using a high-performance FR-PQM metric for distorted pictures as training labels. Subsequently, an end-to-end map between the distorted pictures and their objective quality/similarity maps (labels) is learned, represented as the DEDA that takes the distorted picture as the input and outputs its predicted quality/similarity map. In the DEDA, the pyramid supervision training strategy is used, which applies supervised learning over three scale layers to efficiently optimize the parameters. In the testing procedure, the quality/similarity maps of the testing samples that can help localize distortions can be predicted with the trained DEDA architecture. The predicted quality/similarity maps are then gradually pooled together to obtain the overall objective quality scores. Comparative experiments on three publicly available standard PQM datasets demonstrate that our proposed DEDA metric is in good agreement with subjective assessment compared to previous state-of-the-art OU-BPQM metrics.

最近，基于深度学习的盲图质量测量（BPQM）度量标准已受到广泛关注。但是，由于受主题评级的训练样本数量有限，训练稳健的深度BPQM指标仍然是一项艰巨而具有挑战性的任务。最新的全参考（FR）图像质量测量（PQM）度量标准与人类主观质量得分非常吻合。因此，可以使用它们来近似人类主观质量得分来训练BPQM指标。受此启发，我们为不需要意见的OUQ BPQM提出了一种深层编码器-解码器体系结构（DEDA），该体系不需要人工标记的失真样本即可进行训练。在训练程序中，为避免过度拟合并确保训练和测试样本的独立性，我们首先构造6，000张失真的图片，并使用通过高性能FR-PQM度量标准获得的客观质量/相似度图作为失真标签作为训练标签。随后，学习了失真图片及其客观质量/相似度图（标签）之间的端到端映射，表示为DEDA，DEDA将失真图片作为输入并输出其预测的质量/相似度图。在DEDA中，使用了金字塔监督训练策略，该策略将监督学习应用于三个比例层，以有效地优化参数。在测试过程中，可以使用训练有素的DEDA体系结构预测可以帮助定位变形的测试样本的质量/相似度图。然后将预测的质量/相似度图逐渐合并在一起，以获得总体客观质量得分。