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However, most existing easily portable blind SR image quality assessment (SR-IQA) methods consider only multi-scale depth information in a single domain, neglecting the utilization of multi-scale information in different domains. In addition, the technique of merely altering the feature extraction block does not accurately replicate how the human visual system (HVS) perceives quality. To address these problems, in this paper, we introduce a parallax compensation-driven feature transform for the super-resolution image quality assessment (PCFTSR-IQA) metric. First, the multi-level feature extraction framework based on the Karhunen\u2013Lo\u00e8ve transform (KLT) is specifically presented to transform the inputs to the feature domain for better extraction of structural and detailed features. Second, a set of parallax compensation loss functions is designed to replicate the HVS perception mechanism, which employs parallax compensation to perform constraints and filtering during the feature extraction process for maximum feature utilization. Finally, we train the end-to-end PCFTSR-IQA network for no-reference (NR) SR-IQA. 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