Quality assessment of an image

A convolutional neural network system for quality assessment of an image, comprising:

• a memory having processor-readable instructions stored therein;
• and
• one or more processors configured to access the memory and execute the processor-readable instructions, which, when executed by the one or more processors configures the one or more processors to perform a method, the method comprising:
• generating a plurality of convolutional layers comprising:
• a first convolutional layer configured to receive the image, the image comprising a first plurality of feature maps;
• and
• an ith convolutional layer configured to extract an (i+1)th plurality of feature maps from an ith plurality of feature maps, where 1≤i≤N and N is a number of the plurality of convolutional layers;
• generating a global average pooling layer configured to obtain an (i+1)th feature set of a plurality of feature sets from the (i+1)th plurality of feature maps by obtaining an average of each feature map of the (i+1)th plurality of feature maps;
• and
• generating a plurality of fully connected layers configured to extract a score distribution for a plurality of scores associated with the image from the plurality of feature sets, the plurality of fully connected layers comprising:
• a first fully connected layer configured to generate a first fully connected output of a plurality of fully connected outputs from the plurality of feature sets;
• and
• a kth fully connected layer configured to generate a kth fully connected output of the plurality of fully connected outputs from a (k−1)th fully connected output of the plurality of fully connected outputs by applying the (k−1)th fully connected output to an (N+k)th non-linear activation function where 1≤k≤L, where:
• L is a number of the plurality of fully connected layers,
• a 0th fully connected output comprises the plurality of feature sets, and
• an Lth fully connected output of the plurality of fully connected outputs comprises the score distribution.

A system for quality assessment of an image, the system comprising:

• a memory having processor-readable instructions stored therein;
• and
• one or more processors configured to access the memory and execute the processor-readable instructions, which, when executed by the one or more processors configures the one or more processors to perform a method, the method comprising:
• generating an (i+1)th plurality of feature maps from the image by:
• feeding the image to a first convolutional layer of a plurality of convolutional layers associated with a convolutional neural network, the image comprising a first plurality of feature maps;
• and
• extracting the (i+1)th plurality of feature maps by applying an ith plurality of feature maps to an ith convolutional layer of the plurality of convolutional layers, where 1≤i≤N and N is a number of the plurality of convolutional layers;
• obtaining an (i+1)th feature set of a plurality of feature sets from the (i+1)th plurality of feature maps applying a global average pooling on the (i+1)th plurality of feature maps, the global average pooling comprising obtaining an average of each feature map of the (i+1)th plurality of feature maps;
• and
• extracting a score distribution for a plurality of scores associated with the image from the plurality of feature sets by:
• generating a first fully connected output of a plurality of fully connected outputs by applying the plurality of feature sets to a first fully connected layer of a plurality of fully connected layers associated with the convolutional neural network;
• and
• generating a kth fully connected output of the plurality of fully connected outputs by applying a (k−1)th fully connected output of a plurality of fully connected outputs to a kth fully connected layer of the plurality of fully connected layers,
• where 1≤k≤L, L is a number of the plurality of fully connected layers, a 0th fully connected output comprises the plurality of feature sets, and an Lth fully connected output of the plurality of fully connected outputs comprises the score distribution.