Deep Learning-based Image Quality Assessment Metric for Quantifying Perceptual Distortions in Transmitted Images

Abstract

An Image Quality Assessment (IQA) metric measures the quality degradation of an image and is used to optimize parameters of an image processing algorithm. The IQA score can be also an important indicator of the target downstream application performance. With the popularity of deep learning (DL)-based applications in resource constrained domains, most of the DL computations are outsourced to avail remotely located resources. The image data transmission for this purpose is susceptible to distortions due to the imperfect communication environment. The existing IQA metrics used to evaluate the quality of these images mainly rely on human judgment and do not account for the perceptual distortions that are responsible for the degradation in DL model performance. To address this issue, we propose a convolutional autoencoderbased IQA metric that compares images in low dimensional feature space and can be used to monitor image degradation occurred during data transmission. The simulation analysis shows that the proposed method introduces at best 0.13% error while on average 8% error compared to the application model accuracy. Importantly, the proposed IQA score coincides with the DL model performance on a downstream task and can be used to optimize the parameters of a communication system.

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