Konx: cross-resolution image quality assessment

نویسندگان

چکیده

Abstract Scale-invariance is an open problem in many computer vision subfields. For example, object labels should remain constant across scales, yet model predictions diverge cases. This gets harder for tasks where the ground-truth change with presentation scale. In image quality assessment (IQA), down-sampling attenuates impairments, e.g., blurs or compression artifacts, which can positively affect impression evoked subjective studies. To accurately predict perceptual quality, cross-resolution IQA methods must therefore account resolution-dependent discrepancies induced by inadequacies as well label shifts ground truth. We present first study of its kind that disentangles and examines two issues separately via KonX , a novel, carefully crafted database. paper contributes following: 1. Through we provide empirical evidence caused changes resolution. 2. show objective have scale bias, reduces their predictive performance. 3. propose multi-scale multi-column deep neural network architecture improves performance over previous state-of-the-art models this task. thus both raise address novel research assessment.

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ژورنال

عنوان ژورنال: Quality and user experience

سال: 2023

ISSN: ['2366-0147', '2366-0139']

DOI: https://doi.org/10.1007/s41233-023-00061-8