IMAGE QUALITY ASSESSMENT BY CONVOLUTIONAL NEURAL NETWORK USING THE TID2013 DATABASE
The article is devoted to the problem of automatic image quality assessment by a convolutional neural network when using the common TID2013 image database for training the neural network. The TID2013 database was chosen for the reason that it contains 25 base real-world images, which were distorted from these images using 24 different distortion methods and with 5 distortion levels, creating a sufficiently large database of 3000 images for training the neural network. For each image, an average expert assessment of its quality is given.