The output of event camera is asynchronous non-structured events, which are not convenient for visualization and application. It’s important to develop structured event representation (SER) method to convert events into structured image-like representations. Current SER methods primarily concentrate on the encoding of event attributes or features, while overlooking the event sampling process. Because one moving object will trigger a series of repeated events along its trajectory, overlooking event sampling will result in motion blur in the structured representation under complex motions or scenes. This paper aims to promote the study of SER from a new perspective and proposes a novel adaptive event sampling method. Specifically, a non-latest event suppression (NLES) approach is first proposed to identify the historical repeated events (i.e., redundant events) and the latest events triggered by the same stimulus. Owing to the interference of noise and timestamp perturbation, the latest events may not depict the latest motion state completely. Thus, we next design a redundancy measurement metric (RMM), which measures the ratio between redundant events and latest events, to control the sampling process of redundant events. Finally, by iteratively applying NLES and RMM on the global and local space, a novel redundancy-suppression based event sampling method (RSES) is proposed. RSES is a plug-and-play module that can be integrated with existing SER method. Experimental results show that RSES could realize adaptive event sampling, and effectively improve the visualization and downstream task performance of existing SER method.