Photo quality classification using deep learning

Abstract

The detection of poor quality images for reasons such as focus, lighting, compression, and encoding is of great importance in the field of computer vision. The ability to quickly and automatically classify an image as poor quality creates opportunities for a multitude of applications such as digital cameras, phones, self-driving cars, and web search technologies. In this paper an end-to-end approach using Convolutional Neural Networks (CNN) is presented to classify images into six categories of bad lighting, Gaussian blur, motion blur, JPEG 2000, white-noise, and high quality reference images. A new dataset of images was produced and used to train and validate the model. Finally, the application of the developed model was evaluated using images from the German Traffic Sign Recognition Benchmark. The results show that the trained CNN can detect and correctly classify images into the aforementioned categories with high accuracy and the model can be easily re-calibrated for other applications with only a small sample of training images.