Opinion Unaware Image Quality Assessment via Adversarial Convolutional Variational Autoencoder

Abstract

Image quality assessment is a challenging computer vision task due to the lack of corresponding reference (pristine) images. This no-reference bottleneck has been tackled with the utilisation of subjective mean opinion scores (MOS) termed as supervised blind image quality assessment (BIQA) methods. However, inaccessible opinion score scenarios limits their applicability. To relieve these limitations, we propose to employ reconstruction based learning trained only on pristine images. This permits an implicit distribution learning of pristine images and the deviation from this learned feature distribution is subsequently utilised for unsupervised image quality assessment. Specifically, an adversarial convolutional variational auto-encoder framework is employed with KL divergence, perceptual and discriminator loss. With state-of-the-art results on four benchmark datasets, we demonstrate the effectiveness of our proposed framework. An ablation study has also been conducted to highlight the contribution of each module i.e. loss and quality metric for an efficient unsupervised BIQA.