Some pictorial styles differ dramatically and can be distinguished without scrutinizing the picture, for example, Pointillist vs. Renaissance; or a photograph by a master vs. a casual snapshot. The investigators explore pictorial style using quantitative measurements, leading to a parametric characterization that captures coarse-grain pictorial style. The research has several applications in computer graphics and image analysis, with broad technological impact on society. It can be embedded in image classification and retrieval tools. For digital photography, it can transfer the style of master photographers to casual snapshots, leading to important quality enhancement. In addition, it will lead to novel and intuitive image manipulation tools that act directly on stylistic aspects. Finally, such statistical estimators might help in the future to understand what makes images photorealistic and lead to filters that increase realism. This research project is accompanied by an interdisciplinary course on the "art and science of depiction," and by an inter-disciplinary workshop on image statistics.
The research studies marginal and joint statistics of oriented multiscale decompositions such as steerable pyramids. The investigators develop a new recursive pyramidal decomposition that captures the spatial variations of "texturedness" over an image. They also explore the statistics of edge features, and they devise new non-linear edge-preserving decompositions to prevent haloing artifacts at strong edges during style transfer. Finally, the research explores a new approach to color style characterization based on the notion of naming color category. The purity of a color is defined as its distance to a color prototype such as pure blue, and statistics of this purity are leveraged to assess and enhance color vividness. Success of the style characterization is evaluated by classification tasks (supervised learning) and by style transfer: the relevant statistics of a source image or set of images are enforced in a destination image. The visual modification of the destination image permits the assessment of which stylistic aspects are captured by the statistics, independently of content. The research builds on the synergy between visual perception, image analysis and computer graphics.
