How changing images to greyscale improves model accuracy?
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Converting images to grayscale can enhance model accuracy by leveraging perceptual color differences and reducing computational complexity. Research by Farup et al. demonstrates that converting color images to grayscale based on local perceptual color differences can lead to accurate image reproduction, outperforming other spatial algorithms . Additionally, Bui et al. found that utilizing grayscale images for object classification resulted in higher accuracy compared to RGB images across various classifiers, with the added benefit of reduced computational cost . Moreover, Liu et al. suggest that grayscale pre-filtering can mitigate the effects of imperceptible perturbations in unlearnable examples, improving classifier performance without adversarial training . Therefore, grayscale images offer a simpler yet effective representation for models, enhancing accuracy while streamlining computational requirements.
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| Papers (5) | Insight |
|---|---|
Open access•Posted Content | Changing images to grayscale improves model accuracy by mitigating the effects of unlearnable examples (ULEs) that exploit color, as shown in the paper "Going Grayscale: The Road to Understanding and Improving Unlearnable Examples." |
Patent 07 Jul 2004 31 Citations | Changing images to greyscale on a bi-stable display improves model accuracy by transitioning through reference states like white or middle grey based on the current optical state, enhancing greyscale accuracy. |
27 Jul 2016 37 Citations | Converting RGB images to grayscale improves model accuracy by enhancing classification performance with reduced computational cost, as shown in the study using a CNN-RNN structure and various classifiers. |
01 Sep 2018 4 Citations | Converting images to greyscale using perceptual colour differences enhances model accuracy by translating local colour variations into greylevel differences, improving image reproduction compared to other algorithms. |
Patent 04 May 2016 3 Citations | Converting images to grayscale reduces illumination impact, enhancing identification precision in the convolutional neural network by improving robustness and reducing illumination sensitivity. |
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