Learning picture quality from visual distraction: Psychophysical studies and computational models

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.

Tin dioxide-based nanomaterials as anodes for lithium-ion batteries

The development of new electrode materials for lithium-ion batteries (LIBs) has attracted significant attention because commercial anode materials in LIBs, like graphite, may not be able to meet the increasing energy demand of new electronic devices. Tin dioxide (SnO2) is considered as a promising alternative to graphite due to its high specific capacity. However, the large volume changes of SnO2 during the lithiation/delithiation process lead to capacity fading and poor cycling performance. In this review, we have summarized the synthesis of SnO2-based nanomaterials with various structures and chemical compositions, and their electrochemical performance as LIB anodes. This review addresses pure SnO2 nanomaterials, the composites of SnO2 and carbonaceous materials, the composites of SnO2 and transition metal oxides, and other hybrid SnO2-based materials. By providing a discussion on the synthesis methods and electrochemistry of some representative SnO2-based nanomaterials, we aim to demonstrate that electrochemical properties can be significantly improved by modifying chemical composition and morphology. By analyzing and summarizing the recent progress in SnO2 anode materials, we hope to show that there is still a long way to go for SnO2 to become a commercial LIB electrode and more research has to be focused on how to enhance the cycling stability.