  Image deep features extracted by pre-trained networks are known to contain
rich and informative representations. In this paper, we present Deep
Degradation Response (DDR), a method to quantify changes in image deep features
under varying degradation conditions. Specifically, our approach facilitates
flexible and adaptive degradation, enabling the controlled synthesis of image
degradation through text-driven prompts. Extensive evaluations demonstrate the
versatility of DDR as an image descriptor, with strong correlations observed
with key image attributes such as complexity, colorfulness, sharpness, and
overall quality. Moreover, we demonstrate the efficacy of DDR across a spectrum
of applications. It excels as a blind image quality assessment metric,
outperforming existing methodologies across multiple datasets. Additionally,
DDR serves as an effective unsupervised learning objective in image restoration
tasks, yielding notable advancements in image deblurring and single-image
super-resolution. Our code will be made available.
