The use of remote vision sensors for autonomous decision-making poses the challenge of transmitting high-volume visual data over resource-constrained channels in real-time. In robotics and control applications, many systems can quickly destabilize, which can exacerbate the issue by necessitating higher sampling frequencies. This work proposes a novel sensing paradigm in which an event camera observes the optically generated cosine transform of a visual scene, enabling high-speed, computation-free video compression inspired by modern video codecs. In this study, we simulate this optically passive vision compression (OPVC) scheme and compare its rate-distortion performance to that of a standalone event camera (SAEC). We find that the rate-distortion performance of the OPVC scheme surpasses that of the SAEC and that this performance gap increases as the spatial resolution of the event camera increases.

翻译：在自主决策中使用远程视觉传感器带来了在资源受限的信道上实时传输海量视觉数据的挑战。在机器人学与控制应用中，许多系统可能快速失稳，这通过要求更高的采样频率而加剧了该问题。本研究提出了一种新颖的传感范式，其中事件相机观测视觉场景的光学生成余弦变换，从而实现了受现代视频编解码器启发的高速、免计算视频压缩。在本研究中，我们模拟了这种光学被动视觉压缩方案，并将其率失真性能与独立事件相机进行了比较。我们发现，光学被动视觉压缩方案的率失真性能优于独立事件相机，并且随着事件相机空间分辨率的提高，这种性能差距会进一步增大。