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.

翻译：智能手机摄像头捕获的图像常因退化而质量下降，其中模糊是主要退化类型之一，给下游任务中的图像处理带来了挑战。本文提出一种低计算量的轻量级基于块的特征，用于图像质量评估。利用该方法，我们能够区分模糊图像与清晰图像的退化程度。为此，我们在多种直观的图像特征（如灰度级方差、一阶和二阶梯度、局部二值模式等纹理特征）上训练基于决策树的XGBoost模型。在公开数据集上的实验表明，所提出的低计算方法在验证集上实现了90.1%的平均准确率，与针对该任务微调的计算密集型VGG16网络（平均准确率94%）性能相当。为验证所提特征和模型的泛化能力，我们在BHBID数据集和一个内部数据集上进行了测试，准确率分别达到98%和91%。该方法在CPU上的运行速度比基于VGG16的模型快10倍，且计算复杂度与输入图像大小呈线性关系，使其适用于低计算能力的边缘设备。