We propose a no-reference image quality assessment (NR-IQA) approach that
learns from rankings (RankIQA). To address the problem of limited IQA dataset
size, we train a Siamese Network to rank images in terms of image quality by
using synthetically generated distortions for which relative image quality is
known. These ranked image sets can be automatically generated without laborious
human labeling. We then use fine-tuning to transfer the knowledge represented
in the trained Siamese Network to a traditional CNN that estimates absolute
image quality from single images. We demonstrate how our approach can be made
significantly more efficient than traditional Siamese Networks by forward
propagating a batch of images through a single network and backpropagating
gradients derived from all pairs of images in the batch. Experiments on the
TID2013 benchmark show that we improve the state-of-the-art by over 5%.
Furthermore, on the LIVE benchmark we show that our approach is superior to
existing NR-IQA techniques and that we even outperform the state-of-the-art in
full-reference IQA (FR-IQA) methods without having to resort to high-quality
reference images to infer IQA.