Objective Quality Assessment of Interpolated Natural Images

UIF: An Objective Quality Assessment for Underwater Image Enhancement

Due to complex and volatile lighting environment, underwater imaging can be readily impaired by light scattering, warping, and noises. To improve the visual quality, Underwater Image Enhancement (UIE) techniques have been widely studied. Recent efforts have also been contributed to evaluate and compare the UIE performances with subjective and objective methods. However, the subjective evaluation is time-consuming and uneconomic for all images, while existing objective methods have limited capabilities for the newly-developed UIE approaches based on deep learning. To fill this gap, we propose an Underwater Image Fidelity (UIF) metric for objective evaluation of enhanced underwater images. By exploiting the statistical features of these images in CIELab space, we present the naturalness, sharpness, and structure indexes. Among them, the naturalness and sharpness indexes represent the visual improvements of enhanced images; the structure index indicates the structural similarity between the underwater images before and after UIE. We combine all indexes with a saliency-based spatial pooling and thus obtain the final UIF metric. To evaluate the proposed metric, we also establish a first-of-its-kind large-scale UIE database with subjective scores, namely Underwater Image Enhancement Database (UIED). Experimental results confirm that the proposed UIF metric outperforms a variety of underwater and general-purpose image quality metrics. The database and source code are available at https://github.com/z21110008/UIF.

Quantifying Visual Image Quality: A Bayesian View

Image quality assessment (IQA) models aim to establish a quantitative relationship between visual images and their quality as perceived by human observers. IQA modeling plays a special bridging role between vision science and engineering practice, both as a test-bed for vision theories and computational biovision models and as a powerful tool that could potentially have a profound impact on a broad range of image processing, computer vision, and computer graphics applications for design, optimization, and evaluation purposes. The growth of IQA research has accelerated over the past two decades. In this review, we present an overview of IQA methods from a Bayesian perspective, with the goals of unifying a wide spectrum of IQA approaches under a common framework and providing useful references to fundamental concepts accessible to vision scientists and image processing practitioners. We discuss the implications of the successes and limitations of modern IQA methods for biological vision and the prospect for vision science to inform the design of future artificial vision systems. (The detailed model taxonomy can be found at http://ivc.uwaterloo.ca/research/bayesianIQA/.) Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.