Deep-Learning Approximation of Perceptual Metrics for Efficient Image Quality Evaluation PROJECT TITLE : Efficient Evaluation of Image Quality via Deep-Learning Approximation of Perceptual Metrics ABSTRACT: An important role in the evaluation of complicated Image Processing algorithms is played by image metrics based on human visual system (HVS). As a result of this, the use of the HVS model is restricted to a small number of applications and a small amount of input data. For all of these reasons, real-world settings do not favour such measurements. Deep Image Quality Metric (DIQM), a deep-learning approach to learn the global image quality feature, is proposed to address these challenges (mean-opinion-score). When compared to prior solutions, DIQM is able to efficiently imitate existing visual metrics while reducing computing costs by an order of magnitude. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Depth Guided Model for Dynamic Scene Deblurring Using Anatomy Segmentation's Epistemic Uncertainty for Anomaly Detection in Retinal OCT
