  Despite significant strides in visual quality assessment, the neural
mechanisms underlying visual quality perception remain insufficiently explored.
This study employed fMRI to examine brain activity during image quality
assessment and identify differences in human processing of images with varying
quality. Fourteen healthy participants underwent tasks assessing both image
quality and content classification while undergoing functional MRI scans. The
collected behavioral data was statistically analyzed, and univariate and
functional connectivity analyses were conducted on the imaging data. The
findings revealed that quality assessment is a more complex task than content
classification, involving enhanced activation in high-level cognitive brain
regions for fine-grained visual analysis. Moreover, the research showed the
brain's adaptability to different visual inputs, adopting different strategies
depending on the input's quality. In response to high-quality images, the brain
primarily uses specialized visual areas for precise analysis, whereas with
low-quality images, it recruits additional resources including higher-order
visual cortices and related cognitive and attentional networks to decode and
recognize complex, ambiguous signals effectively. This study pioneers the
intersection of neuroscience and image quality research, providing empirical
evidence through fMRI linking image quality to neural processing. It
contributes novel insights into the human visual system's response to diverse
image qualities, thereby paving the way for advancements in objective image
quality assessment algorithms.
