光学被动视觉压缩的率-失真分析
Rate-Distortion Analysis of Optically Passive Vision Compression
AI总结：
本文提出了一种基于光学生成余弦变换的视觉压缩方法，通过模拟对比发现其率-失真性能优于传统事件相机
摘要：
使用远程视觉传感器进行自主决策面临着在实时条件下通过资源受限的信道传输高量级视觉数据的挑战。在机器人和控制系统应用中，许多系统可以迅速失稳，这会加剧问题，因为需要更高的采样频率。本文提出了一种新的传感范式，其中事件相机观察视觉场景的光学生成余弦变换，从而实现受现代视频编码启发的高速、无计算视频压缩。在本研究中，我们模拟了这种光学被动视觉压缩（OPVC）方案，并将其率-失真性能与独立事件相机（SAEC）进行比较。我们发现，OPVC方案的率-失真性能优于SAEC，并且随着事件相机的空间分辨率增加，这种性能差距也会增大。
英文摘要：
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.
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