TID2013

The paper considers the problem of enhancing image quality using neural networks with different types of multi-level distortions - contrast degradation, adding noise of various natures, image compression, etc. The widespread TID2013 database, which contains both original images and images modified using various types of distortions (25 basic images, 24 types of distortions, and 5 of their levels), was used as the image database for training neural networks. This database was divided into training (480 images), validation (360), and test (120) images.

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.