Abstract:
HVS-based image metrics help evaluate complex image processing algorithms. However, mimicking the HVS is complex and computationally expensive (both in time and memory), limiting its use to a few applications and small input data.
This makes such metrics unattractive in practice. Deep Image Quality Metric (DIQM), a deep-learning approach to learn the global image quality feature (mean-opinion-score), addresses these issues.
DIQM efficiently emulates visual metrics, reducing computational costs by over an order of magnitude.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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