Internet giant Google has introduced a solution to the problem of a long-standing problem in image processing and computer vision.
In “NIMA: Neural Image Assessment” it introduced a deep CNN that is trained to predict which images a typical user would rate as looking good (technically) or attractive (aesthetically).
NIMA relies on the success of state-of-the-art deep object recognition networks, building on their ability to understand general categories of objects despite many variations.
Recently, deep convolutional neural networks (CNNs) trained with human-labelled data have been used to address the subjective nature of image quality for specific classes of images, such as landscapes.
However, these approaches can be limited in their scope, as they typically categorize images to two classes of low and high quality.
“Our proposed method predicts the distribution of ratings. This leads to a more accurate quality prediction with higher correlation to the ground truth ratings, and is applicable to general images,” informs a blog post by Google.
Our proposed network can be used to not only score images reliably and with high correlation to human perception, but also it is useful for a variety of labor intensive and subjective tasks such as intelligent photo editing, optimizing visual quality for increased user engagement, or minimizing perceived visual errors in an imaging pipeline.