With the advent of sensitive and affordable cameras, classical contact-based photoplethysmography (PPG) could be enhanced to the spatial domain. Cost-efficient cameras are available in everyday items such as smartphones or computer webcams. The PPG signal, blood volume changes in the vascularity, can be measured remotely by using the camera as a 2-D-PPG detector. However, the evaluation of the extracted signals has mostly been limited to the pulse rate and sometimes the systolic amplitude. In this work, we motivate to generate images and video sequences based on features from the PPG waveform commonly not extracted via cameras. This is achieved by calculating the features for timeseries extracted from an evenly spaced grid of virtual PPG sensors. We briefly discuss the adaption of conventional PPG algorithms to camera-based PPG imaging (PPGI). The extracted parameters are associated with vessel properties and thus, mapping these to images could lead to enhanced vascular diagnostics. In this work, we test the feasibility of the mapping approach: we present the preliminary results gathered from the analysis of two videos of lab experiments with healthy subjects.