Intelligent Image/Video Editing
Jiaying Liu, Institute of Automation, Chinese Academy of Sciences, China Wenhan Yang, National University of Singapore, Singapore.

Intelligent image/video editing is a fundamental topic in image processing which has witnessed rapid progress in the last two decades. Due to various degradations in the image and video capturing, transmission and storage, image and video include many undesirable effects, such as low resolution, low light condition, rain streak and rain drop occlusions. The recovery of these degradations is ill-posed. With the wealth of statistic-based methods and learning-based methods, this problem can be unified into the cross-domain transfer, which cover more tasks, such as image stylization.

In our tutorial, we will discuss recent progresses of image stylization, rain streak/drop removal, image/video super-resolution, and low light image enhancement. This tutorial covers both traditional statistics based and deep-learning based methods, and contains both biological-driven model, i.e. Retinex model, and data-driven model.

Visual Quality Assessment: Theories, Methodology, and Applications
Yuming Fang, Jiangxi University of Finance and Economics, China Patrick Le Callet, Ecole polytechnique de l’Université de Nantes, France.

In this tutorial, we will overview the trend of visual content perception from the visual quality assessment. In the first section, we briefly introduce the achievements of visual quality assessment during the past decade, and provide the key advantages and disadvantages of existing visual quality assessment metrics. Then we will introduce some of new visual quality assessment methods proposed in our team, mainly including two types: general-purpose and application-specific approaches. Some open problems in visual quality assessment are also discussed for the potential research in the future. In particular, we will present ongoing challenges to deal with uncertainty of ground truth data when developing visual quality assessment models at the age of deep learning.