MIPI CSI-2 v3.0是为期四年的开发阶段的产品，探讨了各种用例，例如物联网（IoT），汽车和无人机。
该规范的新版本具有三个关键功能，这些功能以重要的方式提高了对机器的认识。
让我们从RAW-24开始，以24位精度表示单个图像像素。RAW-24并非用于“人类消费”，而是用于机器消费。该功能旨在使机器能够根据出色的图像质量做出更好的决策。想象一辆完全自动驾驶的汽车，它必须在非常明亮的阳光下行驶，然后进入非常黑暗的隧道。使用RAW-24，车辆可以利用非常精确的图像质量并判断图像上的暗度是无害的阴影还是要避免的车道坑洼。
CSI-2 v3.0中的第二个关键新功能是智能目标区域（SROI）。它用于使用推理算法分析图像并进行精确推论。在最长的时间里，我们训练了医学专业人员来检查MRI扫描以确定是否需要进一步关注-例如，是否存在某些可能映射到肿瘤的区域。如今，许多此类手动过程已被机器推理算法取代。SROI是一种功能，使医疗设备能够更确定地识别医学图像中的诸如肿瘤之类的异常，或者使工厂车间的机器能够更快地识别传送带上的潜在缺陷。同样，具有其分辨率和在后台运行的算法类型的摄像机能够比我们作为人类所能做的更加准确地做出这些推断。
第三个关键的新功能是统一串行链接（USL）。从历史上看，从I / O角度看，MIPI成像导管需要许多导线。但是，如果我们想采用为手机开发的摄像头解决方案，并将其部署在存在局限性的平台上，例如通过非常狭窄的铰链布线这些电线，则需要一种更有效的方法。USL在这里要做的是封装成像像素并控制图像传感器模块和应用处理器之间的传输数据，从而极大地减少了所需的电线数量，并使MIPI成像传感器可以部署在移动形式因素（例如内容）以外的广泛平台上创建设备（笔记本电脑，笔记本电脑等），物联网系统，无人机甚至可能是针对汽车用例的舱内监控。
Three Critical New Features
MIPI CSI-2 v3.0 is the product of a four-year development phase exploring various use cases, such as the Internet of Things (IoT), automotive and drones.
The new version of the specification delivers three key features, which contribute to machine awareness in important ways.
Let’s start with RAW-24 for representing individual image pixels with 24-bit precision. RAW-24 it is not meant for “human consumption”—it is meant to be consumed by machines. The capability is intended to enable machines to make better decisions from superior image quality. Imagine a fully autonomous car that has to navigate through very bright sunlight and then enters a very dark tunnel. With RAW-24, the vehicle can leverage very precise image quality and decipher whether darkness on an image is a harmless shadow or a pothole in the roadway to be avoided.
The second crucial new feature in CSI-2 v3.0 is Smart Region of Interest (SROI). It’s for analyzing images using inferencing algorithms and making accurate deductions. For the longest time, we have trained medical professionals to look at MRI scans to determine if there is something that requires further attention—for instance, if there is some region that perhaps maps to a tumor. Today, many of these types of manual processes are being replaced by machine inferencing algorithms. SROI is a capability for enabling, for example, medical devices to more surely recognize anomalies such as tumors in medical images—or enabling machines on a factory floor to more quickly identify potential defects on a conveyor belt. Again, the cameras with their resolutions and the type of algorithms that are running in the background are able to make these deductions far more accurately than what we are able to do as humans.
The third key new feature is Unified Serial Link (USL). Historically, a MIPI imaging conduit from an I/O perspective demands many wires. But if we want to take the camera solution we developed for mobile phones and deploy it on platforms where there are limitations like, say, routing these wires through very narrow hinges, a more efficient approach is needed. What USL does here is encapsulate imaging pixel and control transport data between an image sensor module and application processor, dramatically minimizing the number of wires needed and enabling MIPI imaging sensors to be deployed on a broad range of platforms beyond mobile form factors, such as content creation devices (notebooks, laptops, etc.), IoT systems, drones and perhaps even in-cabin monitoring for automotive use cases.
