arXiv:2404.18162v1 Announce Type: new
Abstract: 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.
Visual quality assessment is an essential aspect of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. Understanding how humans perceive and evaluate the quality of visual content is crucial for developing algorithms and technologies that can automatically assess and enhance visual quality. This study, which employed functional MRI (fMRI), provides valuable insights into the neural mechanisms underlying visual quality perception.
The Complexity of Image Quality Assessment
The study found that assessing image quality is a more complex task for the human brain compared to content classification. While both tasks involve visual analysis, fine-grained analysis of image quality requires enhanced activation in high-level cognitive brain regions. This suggests that the brain engages in more in-depth processing to evaluate the quality of visual content.
By investigating brain activity during image quality assessment, the researchers have shed light on the multi-disciplinary nature of visual quality perception. The study involved a combination of neuroscience, psychology, and computer science, highlighting the need for an interdisciplinary approach in understanding human perception and cognitive processing.
The Brain’s Adaptability to Different Visual Inputs
The research demonstrates the brain’s adaptability to different qualities of visual information. When presented with high-quality images, the brain primarily utilizes specialized visual areas for precise analysis. This finding aligns with what we understand about the processing hierarchy in the visual system, where lower-level visual areas extract low-level features, such as edges and contours, while higher-level visual areas analyze more complex visual patterns.
In contrast, when presented with low-quality images, the brain recruits additional resources, including higher-order visual cortices and related cognitive and attentional networks. This suggests that the brain tries to compensate for the lack of detailed visual information by engaging broader cognitive and attentional processes. These processes might involve pattern recognition, inference, and top-down influences to decode and recognize ambiguous signals effectively.
The Intersection of Neuroscience and Image Quality Research
This study represents a pioneering effort to bridge the gap between neuroscience and image quality research. By using fMRI to link image quality to neural processing, the researchers have provided empirical evidence for the neural mechanisms underlying visual quality perception. This intersection of neuroscience and image quality research opens up new possibilities for objective image quality assessment algorithms.
Objective image quality assessment algorithms play a crucial role in various fields, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By developing algorithms that can automatically assess and enhance visual quality, we can improve the user experience across these domains.
In conclusion, this study contributes novel insights into the complexity of image quality assessment and the brain’s adaptable processing of visual inputs. It highlights the multi-disciplinary nature of understanding visual quality perception and paves the way for advancements in objective image quality assessment algorithms. The intersection of neuroscience and image quality research has the potential to revolutionize our understanding of visual perception and enhance the technologies that rely on it.