Image quality assessment using similar scenes as reference

A system for image quality assessment of non-aligned images, comprising

• a first deep path portion of a convolutional neural network having a set of parameters;
• a second deep path portion of the convolutional neural network sharing a set of parameters with the first deep path convolutional neural network, wherein weights are shared between the first and second deep paths of the convolutional neural network to support extraction of a same set of features, and wherein non-aligned reference and distorted images are respectively provided to the first and second deep paths of the convolutional neural network for processing;
• a concatenation layer connected to both the first and second deep paths convolutional neural network;
• a fully connected layer connected to the concatenation layer to receive input from both the first and second deep paths of the convolutional neural network, generating an image quality assessment as a linear regressor;
• and
• outputting an image quality score.

A method for generating a training set for image quality assessment of non-aligned images, comprising the steps of using raw images and providing non-aligned reference and distorted images by synthesizing a set of non-aligned images;

• inputting reference images to a first deep path of a convolutional neural network having a set of parameters;
• inputting distorted images to a second deep path of the convolutional neural network sharing a set of parameters with the first deep path convolutional neural network;
• sharing weights between the first and second deep path of the convolutional neural network to support extraction of a same set of features in each neural network;
• in a fully connected layer connected to receive input from both the first and second deep paths of the convolutional neural network, generating an image quality assessment as a linear regressor;
• and
• outputting an image quality score.