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Computer Science, Computer Vision and Pattern Recognition

Assessing Image Quality in Various Scenarios with a Self-Adaptive Hyper Network

Assessing Image Quality in Various Scenarios with a Self-Adaptive Hyper Network

The study explores how people perceive and agree on images when presented with pairs of virtual objects in different contexts. The research aimed to investigate if observers can accurately assess the similarity between two images, especially when the images are composed of complex stimuli.

Stimuli

The virtual objects rendered were simple spheres placed on a plane, either close or far away from the observer. This allowed the researchers to examine how the distance between the objects affects the observers’ perception and agreement.

Expected Observer Agreement

The expected observer agreement (ω) was calculated by averaging the individual observers’ agreement scores. A perfect observer would have an agreement score of 1, while a random observer would have an agreement score of 0.5. The researchers removed data from individuals who scored lower than 0.5 in at least one experiment, as they were deemed to have misunderstood the task.

Hypothetical Perfect Observer

A hypothetical perfect observer was defined as someone who makes the same choices as the average observer. In this case, the expected agreement score for a perfect observer would be 1.

Conclusion

The study demonstrates that observers can accurately assess the similarity between two images when presented with pairs of virtual objects in different contexts. The research highlights the importance of considering the distance between objects when assessing their similarity. Furthermore, the findings suggest that people can become proficient at this task with practice.

Analogy

Understanding the concept of observer agreement is similar to evaluating a restaurant’s food quality based on the opinions of multiple diners. While each diner may have their unique preferences, they can still agree on the overall quality of the meal. Similarly, observers can agree on the similarity between two images despite having different perceptions of them.
By using this analogy, the complexity of the concept of observer agreement can be demystified, and its essence captured in a simple yet effective way.