  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 degradation. To this end, we train a
decision-tree based XGBoost model on various intuitive image features like gray
level variance, first and second order gradients, texture features like local
binary patterns. Experiments conducted on an open dataset show that the
proposed low compute method results in 90.1% mean accuracy on the validation
set, which is comparable to the accuracy of a compute-intensive VGG16 network
with 94% mean accuracy fine-tuned to this task. To demonstrate the
generalizability of our proposed features and model we test the model on BHBID
dataset and an internal dataset where we attain accuracy of 98% and 91%,
respectively. The proposed method is 10x faster than the VGG16 based model on
CPU and scales linearly to the input image size making it suitable to be
implemented on low compute edge devices.
