Potential of deep features for opinion-unaware, distortion-unaware, no-reference image quality assessment

TL;DR

This paper investigates using deep features from a convolutional autoencoder for no-reference image quality assessment, showing it outperforms traditional hand-crafted feature methods without requiring subjective scores.

Contribution

It introduces a novel approach leveraging deep autoencoder features for IQA, eliminating the need for supervised training on human opinion scores.

Findings

Deep features outperform hand-crafted features for blurring, noise, and compression artifacts.

The method shows strong correlation with human opinion scores.

It demonstrates the potential of unsupervised deep feature-based IQA methods.

Abstract

Image Quality Assessment algorithms predict a quality score for a pristine or distorted input image, such that it correlates with human opinion. Traditional methods required a non-distorted "reference" version of the input image to compare with, in order to predict this score. However, recent "No-reference" methods circumvent this requirement by modelling the distribution of clean image features, thereby making them more suitable for practical use. However, majority of such methods either use hand-crafted features or require training on human opinion scores (supervised learning), which are difficult to obtain and standardise. We explore the possibility of using deep features instead, particularly, the encoded (bottleneck) feature maps of a Convolutional Autoencoder neural network architecture. Also, we do not train the network on subjective scores (unsupervised learning). The primary requirements for an IQA method are monotonic increase in predicted scores with increasing degree of input image distortion, and consistent ranking of images with the same distortion type and content, but different distortion levels. Quantitative experiments using the Pearson, Kendall and Spearman correlation scores on a diverse set of images show that our proposed method meets the above requirements better than the state-of-art method (which uses hand-crafted features) for three types of distortions: blurring, noise and compression artefacts. This demonstrates the potential for future research in this relatively unexplored sub-area within IQA.