标签: rgb

站在技术的维度，“下沉”趋势代表民心所向，HUD亦如此。 当汽车座舱上升为人类生活的“第三空间”，HUD（Head Up Display，车载抬头显示系统）正从小众且昂贵的选配市场，逐渐过渡到更多车型的标配。 其功能正从仅显示速度、里程等数字信息，演变成集地图导航、前方道路引导……甚至是AR加持下POI兴趣信息推荐。 智能汽车第一屏，不外如是。 1、 持续“上量”，持续“下沉” 2组数据。 根据佐思汽研统计，2023年1-6月中国市场（不含进出口）前装装配HUD的乘用车为87.9万辆，同比增长45.6%；渗透率为9.5%，同比增加2.7个百分点。其中，2023年二季度，前装装配HUD的乘用车为50.4万辆，同比增长65%；渗透率为9.7%，同比增加3.5个百分点。 2021-2023年各季度中国乘用车新车HUD装配量及同比增长 *图源：佐思汽研 而在更早期的预测中，佐思汽研预计2023年第二季度中国乘用车市场HUD装配量为46.4万台，实际数据则为50.4万台，高于预期。 同时，截止今年6月底，中国乘用车市场已有超过55个品牌旗下车型提供W/AR-HUD的标配或选配装置。 一句话，HUD上车正在加速。 第二，以HUD领域中第三代产品AR-HUD为例，一方面，2023年1-6月乘用车装载量为6.35万辆，同比增长81.4%，全年总量有望翻番；另一方面，从终端价格来看，2023年正式上市的多款配备AR-HUD的国内车型开始下沉到15~20w价位的走量车型区域 。 主机厂AR-HUD上车时间表 *图源：佐思汽研 一句话，HUD正在从选配过渡到标配，走向大众。 “我们站在上游角度，也能感受到HUD的热度，越来越流行，”艾迈斯欧司朗高级系统方案工程师周健华谈到，“这源于HUD提供了多种价值，比如行车数据‘前置’带来的安全度提升；或者车内氛围展示；或者观影等车生活赋能。” 消费者可直观感受到的多种价值提供，是用户评估整车价值感提升的重要环节，更是“上量”和“下沉”的底层逻辑。 直击动感光波圆桌派@2023 CIOE上线 首期聚焦人机交互与协同新趋势，看大咖聊HUD！ 2、 HUD用户评价的硬件支撑？ 对HUD来说，用户体验主要是从硬件和软件（含交互逻辑）两个方面来看。 软件方面，除了软件的稳定性常规考量之外，重在交互逻辑和交互界面的合理性； 硬件方面，除了需要考量HUD的稳定性、可靠性、耐久性之外，还需要看显示画面的画质，以及眩晕感。 而画面的亮度、对比度、均匀度、色彩、细腻程度，这些画面质量相关的体验，跟PGU（图像生成单元）息息相关。 毕竟车载HUD的使用需要考虑夏日正午和夜间等极限场景，对画面的最大最小亮度要求都极其严苛。 “目前市面上较流行的HUD方案，主要有以下这3类。第一，DLP+RGB LED；第二，LCoS+RGB LED，第三，TFT LCD+白光LED，不同方案中的光源部分我们都能覆盖，” 周健华说道。 展开来说。 DLP+RGB LED的方案，优势主要在于技术相对成熟，色彩表现好，但成本较高，同时从供应链安全的角度来看，很多厂商仍存在担忧。 LCoS+RGB LED的方案针对上一方案进一步解决了供应链安全问题，同时色彩表现好，但一方面相比DLP方案技术成熟度要更低，另一方面，尽管LCoS panel的价格相比DLP有所降低，但是如果考虑配套驱动系统，是否能达到整体方案的性价比提升，仍有待考量。 就性价比来看，TFT LCD+白光LED的方案最优，技术路线成熟，也能够满足大部分的需求，目前仍是国内外行业内的主流选择。 但由于其颜色表现性、对比度等细节欠佳。此外由于“阳光倒灌”的风险，因此TFT方案的热失效、热管理、热设计的难度也比较大。也正是由于这些缺陷，才激发了行业内对更多技术路线的探索。 “除了上述相较成熟的技术，很多客户也在跟我们沟通更多可能方案。” 比如说，Micro LED，但从技术成熟度和性价比来看（特别是三色Micro LED），目前这一方案距离落地仍需一段时间。 其次，还有RGB Laser+MEMS的方案，通常称作LBS方案， “确实已有客户前来和我们沟通LBS方案，我们也针对这套方案对应提供了光源产品做配套。” 但就目前来看，这套方案所面临的技术难题仍不少。 第三，就是Holographic，全息方案。据周健华介绍，这个方案可以从更长远的视角考虑，它不仅仅是光学、电子系统本身，未来更将承载和AI技术结合的可能性。 当然还有光波导方案，“目前光波导技术在AR HUD方案商仍有不少技术问题待解决，我们也非常乐见光波导技术早日成熟。” 如果这样的方案发展成熟并逐步推向市场的话，那对于PGU这一端来说也会有不同需求产生， “对艾迈斯欧司朗来说，我们也会进一步推出不同的光源产品进行相应配套。” 总结来看，像当下较为成熟的TFT LCD，DLP和LCoS背投技术，实际上就是PGU的技术路线之争。 3、 LED+激光光源，两手抓 不论是LED光源，还是激光光源，艾迈斯欧司朗都主打 以不同芯片满足不同需求，充分满足客户需要。 就TFT LCD来讲，3款芯片产品（1平方毫米、0.5平方毫米、0.25平方毫米）对应不同大小的功率，而不同的芯片满足不同的客户/场景需求。 因为当一个方案最终落地时，需要结合实际场景中多种多样的考量，“比如有些客户希望光源功率密度大一点，这样一来，所用LED数量就会减少，有些客户则源于别的因素，更倾向于采用小功率LED，所以对应于这些需求我们都会有不同产品出来。” 同样对于DLP来讲，就车载HUD方案，主要看重TI 0.3英寸、0.55英寸的DMD，“但将要有一款0.46英寸的DMD产品出来，所以针对这3款DMD，我们的产品策略一样，一一对应。” 有关LED产品未来路线？ 就白光LED来说，首先，未来推出的一些新品会将封装集中到SYNIOS ® P2720系列封装，如此一来，相比过去的大功率封装，能进一步提升产品的性价比； 其次，客户对于能够提高对比度的Local Dimming的需求日渐明显，特别是TFT LCD方案的一个很大缺陷就是它的对比度相比其他方案更弱； 再次，由于TFT LCD方案颜色表现形式略差，艾迈斯欧司朗也在考虑采用不同的荧光粉技术来对应改善。 针对RGB LED，则会有不同的调整。 RGB LED当前主要应用于DLP方案。比如，针对0.55英寸的DMD平台，考虑将原先匹配的芯片产品旋转一个角度（如下图所示），从而使得光源的长宽比跟DMD的长宽比更加匹配，以提高光源利用率以及效率； 此外，为了缩减光学成本，也有对应2通道产品LE BR Q7WM.03，即将红光和蓝光合封在一起，绿光单独封装。据悉，这款产品的发光效率使用了艾迈斯欧司朗的新技术， “从发光效率来讲，已经处在一个非常先进的水平。” 当然，对HUD来说，激光光源的方案必不可少，这源于它多个角度的优势。 激光光源的带宽更窄，因此在类似光波导方案中，激光才是更合适的光源。同样对于LCoS方案来讲，由于激光本身具有偏振性，因而相比LED来说它也是更理想的光源，会有效提升效率。 “激光光源主要分为2大类，多模激光二极管和单模激光二极管，多模的特点就是功率更高，比如蓝光，目前我们可以做到5W；相应单模产品的功率会低一些，一般是mW级，不会超过1W。” 据悉，艾迈斯欧司朗也在考虑开发RGB 3色的车规激光光源，会将红、绿、蓝三色激光产品先后过车规，从而形成全套RGB激光方案，主要针对的还是之前提到的LBS方案。

目前计算机显示器在RGB画面案与PAL制影像摄像头输入(PIP,POP)一般采用x86架构PC或平板为主。这样的操作不仅需要搭配一个操作系统,而且会使整体的效能牺牲掉许多效能, 且造成开机与关机时间过长。不适合工厂,消防安全系统,警用隧道,高楼建筑侦查等作业场合. 使用51架构的双输入芯片可以轻易的解决这个困扰.例如Averlogic AL600, 具有以下优势： 1.AL600本身具有双输入(VIDEO,RGB)整合，不需WINDOWS操作系统支持可作PIP,POP双画面同时显示。 2.可实时开机,此功能可以让系统处于极端稳定的环境,没有被计算机病毒攻击的困扰,可应用在各种工业或军用极端环境上，并可转换为模拟输出给不同显示器使用. 3.可任意定位PIP画面在主画面的不同位置,完全不占用作业时间,. 4.AL600也可以作为VIDEO,RGB的切换画面开关,可做各种不同的组合.如VIDEO迭加在RGB画面,或是VIDEO迭加在VIDEO上面.内建OSD可供开发者自行开发字型. 想了解更多AL600免费数据，可以随时找深圳茂晶公司询问.

历史修改记录 时间 记录 版本号 2012/4/25 初稿完成 1.0                                    目   录 历史修改记录... 2 目   录... 3 1        概述... 4 2        主控芯片CSU8RF2111. 5 2.1        功能描述... 5 2.2        主要特性... 5 3        功能说明... 6 4        操作说明... 7 5        原理图... 9     带记忆的 RGB LED 灯 1         概述 使用我司CSU8RF2111S制作的具备记忆功能RGB灯，如 图 1 所示。通过红外摇控控制RGB 灯，发出不同颜色的光。 2         主控芯片CSU8RF2111 2.1功能描述 CSU8RF2111 是一个8 位CMOS 单芯片FLASH MCU，内置1K*16bit 的FLASH 程序存储器。 2.2主要特性 ● 高性能的RISC 中央处理器(CPU)8位单片机MCU ● 内置1K*16bit程序存储器FLASH ● 96字节数据存储器（SRAM） ● 56 字节的E2PROM，用于数据存储 ● 只有43 条单字指令 ● 4 级存储堆栈 ● 支持在线烧录 ● 上电复位(POR) ● 上电复位和硬件复位延迟定时器（40ms） ● 内带低电压复位（LVR） ● 定时器0 8位可编程预分频的8位定时计数器 ● 定时/计数器1 8位可编程预分频的8位分频器 ● 扩展型看门狗定时器（32K WDT）可编程的时间范围 ● 内带16MHz 振荡器 ● 外部 32.768KHz 晶振（RTC）或 4MHz ~16MHz 晶振 ● 电压工作范围 VDD 2.3V~5.5V ● 11 位双向I/O口，1 位输入口 ● 1 路蜂鸣器输出 ● 1 路PWM 输出 ● 2 个内部中断，1 个外部中断 ● 5 个具有唤醒功能的输入口 ● 低电压检测（LVD）引脚，内部提供 2.4V、3.6V 电压比较 ● 四个开漏输出口 ● MCU工作电流 正常模式1mA@4MHz（工作电压3V）正常模式10uA@32KHz（工作电压3V） 休眠模式下的电流小于1uA ● 8-pin SOP/DIP   3         功能说明 RGB LED 灯具备以下特点： 2.1 可在直流、交流电源下工作； 2.2 可发出16种色彩光，4种色彩变化的光，并具备4 级亮度调节功能； 2.3 状态记忆：断电后再次上电时，继续接上一次的断电状态运行； 2.4 过零检测： 当采用交流供电时，可给RGB LED 灯接入过零检测信号，即可按外部过零信号50Hz 为时基，实现多个RGB LED 灯的同步运行功能。 4       操作说明          未完，详情见附件

  Next we point the spectrometer at another area (again, let's say a pinkish area to keep with our previous example) and we tweak our light sources until the new RGB values being reflected from the pinkish area are the same as the old RGB values from the brownish area. Initially we are amazed to see that all of the colors on the panel appear to be unchanged. Now we take a piece of white card that covers the entire panel apart from a cut-out that reveals only the original pinkish area ... which magically changes into the brownish color. But when we remove the white card to reveal the entire panel, the pinkish area returns to its original pinkish hue. How can this be? In fact, what's happening is that your brain maintains a three-dimensional color-map in which every color is weighted in relation to every other color. Thus, when you can see the whole panel, your brain automatically calculates all of the color relationships and adjusts what you're actually seeing to match what it thinks you should be seeing. By comparison, if you can see only one shape, then your brain has no other recourse than to assume that this shape's color is determined by the red, blue, and green components that are being reflected from the shape. It's only if you can see a shape's color in the context of all of the other shapes' colors, then your brain does some incredibly nifty signal processing, determines what colors the various shapes should be, and corrects all of the colors before handing the information over to the conscious portion of your mind. All I can say is that you really have to see this to believe it – speaking of which... Performing this experiment for ourselves I would love to recreate this experiment and post it on YouTube so that everyone can see it for themselves. As we see from the discussions above, there are main three elements to this experiment: the three RGB light sources, the panel with the multicolored geometric shapes, and the spectrometer (or whatever we decide to use). The Lights: I don't think that laying my hands on three stage lights (with individual dimmers) along with three pure color RGB filters will pose a major problem. The Multicolored Panel: The panel with the multicolored geometric shapes is another issue. If we make it out of board – say 1.83 x 1.83 m – then it's going to be a pain to move around (suppose, for example, that I wanted to replicate this experiment as ESC or DAC next year). Also, if we have say 100 colored geometric areas (squares, rectangles, L-shapes, T-shapes) ... then this is going to cost a fortune in paint, because we would be using only a dribble from each can. But earlier today I had an idea... In the building in which you find my little office, I share the bay with a company called Out of the Box Exhibits . This is rather cool – they make incredibly low-cost trade show exhibits using cardboard structures clad with a tough canvas material upon which can be painted any design their customers desire. Even better, their graphics expert – Bruce Till – sits in the office next to mine. With Bruce's help, I could easily get a vibrant, multicolored canvas panel designed and printed, so all that remains is... The Spectrometer (or Equivalent): I remember looking into these a couple of years ago and they were not cheap. But technology has progressed in leaps and bounds, so there are several solutions that spring to mind. One possibility would be to somehow connect a digital camera to a PC running some sort of software application such that you could display what the camera was seeing (like our multicolored panel) in real-time on the PC screen. Another aspect to the software application would be that it would display a set of cross-hairs on the screen and that you could move these cross-hairs using your mouse or the arrow keys. Wherever the cross-hairs are on the screen, you would see a readout of the corresponding RGB values "under" the cross-hairs. An even simpler option (in some respects) would be to have a special application running on my iPad, which already has an inbuilt camera. But I know nothing about building iPad apps and I have no clue where to turn... So now it's over to you. Do you have a better alternative to my camera-PC combo or my creating an iPad app concept? Maybe you know someone who can create iPad apps. If you do have any ideas, please feel free to post a comment .... our operators (well, me, actually) are standing by...

I previously wrote a column about a really amazing color vision experiment. I'm now a little closer to recreating this, but I could still use a little help... When I first saw this experiment performed a couple of decades ago on a television program it completely blew me away. But before we plunge headfirst into the fray with gusto and abandon, let's start with a very high-level intro as to the way in which we perceive objects as having different colors. Of course the way in which color vision works is amazingly complicated, so if you want to learn more, may I be so bold as to point you at my paper Color Vision: One of Nature's Wonders , which currently ranks #6 in a search for "Color Vision" on Google and is also found as an external link from the Color Vision page on the Wikipedia (sorry, I must admit to being quite proud of this paper). Setting the scene For our purposes here, let's begin by considering the first image below. The idea is that white light is made up of all the colors in the spectrum, but that we can approximate it using a mixture of red, green, and blue (RGB). In the case of an object like a tomato, which we perceive as being red, what is actually happening is that it is absorbing any green and blue light components and reflecting the red, so it's the red component that hits our eyes. Similarly, something we perceive as being green – like grass – absorbs the red and blue light components and reflects the green, so it's the green component that hits our eyes.   In the case of a pure white surface, this reflects all of the color components. By comparison, a pure black surface absorbs all of the color components (note that we're only talking about the visible spectrum here). Of course most real-world objects are more subtle than this in that they do not appear to us as primary colors, but instead comprise different amounts of red, green, and blue. Now, let's suppose we have two objects called Object A and Object B. Let's assume that it's a sunny day, we take our objects outside to look at them, and we see that Object A appears to be a brownish color while Object B has a pinkish hue. We return inside and place Object A and Object B on a table – surrounded by other objects of every color under the sun – and we illuminate them with three RGB light sources that, together, approximate a single source of white light. Under these lighting conditions, it comes as no surprise to discover that we still see Object A and Object B as being brownish and pinkish, respectively. Suppose we use a spectrometer (or something of that ilk) to measure the amounts of red, green, and blue light being reflected from Object A and Object B. It probably wouldn't shock us to discover that the RGB values being reflected from Object A are different to the RGB values from Object B. So, our knee-jerk reaction is that the RGB values being reflected by Object A uniquely define its brownish color. Similarly, the RGB values being reflected by Object B uniquely define its pinkish hue. This is where things start to get interesting... suppose that we fiddle with our three light sources, making one brighter and another dimmer and so forth until the spectrometer shows that the new RGB components being reflected from Object B are the same as the original RGB components we used to see being reflected from Object A. In this case, our knee jerk reaction is that Object B will now appear brownish (rather than pinkish) while Object A will seem to be some color other than its original brownish... What if I told you that in fact both objects appear to retain their original colors? Now read on... The experiment that inspired me Let's turn our attention to the experiment that originally inspired me. Consider the image below. The idea is that we have a panel formed from a large number of geometric shapes, each of which is a different color (actually, there's no reason why some areas should not share the same color). This panel is illuminated by three RGB light sources and we have a spectrometer that we can point at any of the areas so as to determine the RBG components that are being reflected by that area.   Originally, our three light sources are set up such that together they approximate a white light source. First we use our spectrometer to measure the RGB values being reflected from one area (let's say a brownish area to keep with our previous example).