Perceptual Quality Assessment of Real-World Images and Videos

Abstract:

​The development of several online social-media venues and rapid advances in technology by camera and mobile device manufacturers have led to the creation and consumption of a seemingly limitless supply of visual content. However, a vast majority of these digital images and videos are often afflicted with annoying artifacts during acquisition, subsequent storage and transmission over the network. Existing automatic quality predictors are designed on unrepresentative databases that only model single, synthetic distortions (such as blur, compression, and so on). However, these models lose their prediction capability when evaluated on real world camera pictures and videos captured using typical real-world mobile camera devices that contain complex mixtures of multiple distortions. Pertaining to over-the-top video streaming, all of the existing quality of experience (QoE) prediction models fail to model the behavioral responses of our visual system to a stimuli containing stalls and playback interruptions.

In her talk, She will focus on two broad topics: 1) construction of distortion-representative image and video quality assessment databases and 2) design of novel quality predictors for real world images and videos. The image and video quality predictors that she present in this talk rely on models based on the natural scene statistics of images and videos, model the complex non-linearities and linearities in the human visual system, and effectively capture a viewer’s behavioral responses to unpleasant stimuli (distortions).