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Comparison of runtimes across different methods. We take 5 different images from KBD, resize it to different sizes and record the time required to run inference on these. It can be seen that the best performing feature LBP Grid 7x7 is 10 times faster than VGG
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Images captured through smartphone cameras often suffer from degradation, blur being one of the major ones, posing a challenge in processing these images for downstream tasks. In this paper we propose low-compute lightweight patch-wise features for image quality assessment. Using our method we can discriminate between blur vs sharp image degradatio...
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... XGBoost model, we measure the time required to extract the features and perform the classification. For vgg16 we set the batch size to 1 and record the time elapsed for the forward pass. For all the algorithms, we measure the time required to process 5 different images, we repeat this experiment for 10 runs and report the mean of the 10 runs in Fig. 4. We also test our extracted features using support vector machines (SVM) classifiers and compare the performance to XGBoost classifier. They perform 3, 3.4, 3.4 percentage points (accuracy) lower than XGBoost in case of Grid 7x7, Grid 7x7 + Global LBP, LBP Grid 7x7. This shows that the extracted features are discriminative across ...
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