Visual Preference Assessment on Ultra-High-Definition Images

With the recent evolution of ultra-high-definition (UHD) display technology, viewers can enjoy high-resolution content more realistically over TVs, virtual reality, portable, and wearable devices. To increase the visual attraction viewers perceive, post-processing of video content has been more powerfully conducted in such commercial devices. In this paper, we define a new terminology visual preference to quantify viewer perceptual preferences in a certain viewing environment with UHD images processed using sharpness and contrast enhancements. Viewers' visual preference for UHD images depends on the spatial resolution afforded by the UHD display, which in turn depends on the viewing geometry of the display resolution, display size, and viewing distance. In addition, viewers can perceive different degrees of quality and sharpness according to the content enhancement type and level, which leads to variation in the statistical dynamics of spatial image information. In this paper, we explore a novel methodology called the visual preference assessment model (VPAM) that accounts for content enhancement features, diverse viewing geometry, and statistical dynamics variation. The VPAM is a no-reference assessment method designed using an elaborate subjective preference assessment with support vector regression as the machine learning algorithm. The VPAM far outperforms previous methods in terms of correlation, 0.45-0.56, with the visual preference assessment.