Scale-invariance is an open problem in many computer vision subfields. For example, object labels should remain constant across scales, yet model predictions diverge in many cases. This problem gets harder for tasks where the ground-truth labels change with the presentation scale. In image quality assessment (IQA), downsampling attenuates impairments, e.g., blurs or compression artifacts, which can positively affect the impression evoked in subjective studies. To accurately predict perceptual image quality, cross-resolution IQA methods must therefore account for resolution-dependent errors induced by model inadequacies as well as for the perceptual label shifts in the ground truth. We present the first study of its kind that disentangles and examines the two issues separately via KonX, a novel, carefully crafted cross-resolution IQA database. This paper contributes the following: 1. Through KonX, we provide empirical evidence of label shifts caused by changes in the presentation resolution. 2. We show that objective IQA methods have a scale bias, which reduces their predictive performance. 3. We propose a multi-scale and multi-column DNN architecture that improves performance over previous state-of-the-art IQA models for this task, including recent transformers. We thus both raise and address a novel research problem in image quality assessment.
翻译:许多计算机视觉子字段的缩放差异是一个公开的问题。 例如, 对象标签应该保持不同尺度的常态, 但模型预测在许多情况下会不同。 对于地面真实标签随着演示规模的变化而变化的任务, 这个问题会变得更为困难。 在图像质量评估( IQA) 中, 缩小缩放缩放缩放缩略图减缩放障碍, 例如模糊或压缩人工制品, 这会积极影响主观研究中产生的印象。 为了准确预测视觉图像质量, 交叉分辨率 IQA 方法必须说明模型缺陷以及地面真相的感知标签变化引起的基于分辨率的错误。 我们提出首次研究其类型会分解的研究结果, 并通过KonX(一个新颖的、经过仔细设计的跨分辨率 IQA 数据库) 单独研究这两个问题。 本文提供以下资料: 1. 通过 KonX, 我们通过实验性能证据, 说明由于演示决议的变化而导致的标签变化。 2. 我们显示, 目标的 IQA 方法存在规模偏差, 从而降低其预测性能。 3. 我们提出一个多级和多级的图像模型, 因此, 改进了最新的图像结构, 改进了最新的IQNNN 。