RGB is outdated for artificial intelligence and broadcasting

After spending dozens of nights without sleep Yop Color coding formats, realized how small information is about this wonderful format. However, it can be incredibly useful for those participants in the P2P video flow or processing video flows with artificial intelligence.
At first glance, RGB and Yop It may seem to be just different ways to represent the color. But under this distinction lies a continuous battle: comfort against efficiency, accuracy for performance, perfect perception against pressure without visual loss. One may assume that RGB He is the undisputed property of color spaces – after all, cameras, screens and most nerve networks work. However, in the world of video flow and coding, Yuv takes the initiative, hiding under the cover is a series of complex bars that allows us to watch videos without delay, provide GB of data, and accelerate actual time processing.
But what if you want to bridge these two worlds? How was artificial intelligence models trained on RGB video flows in Yuv? Why are coding programs to work with RGB? Is it possible to achieve the ideal balance between these formats? Here, I will help you diving the reason that RGB and Yuv are like two boxers of two different weights, forcing you to meet in the same episode of broadcasting video and artificial intelligence technology.
RGB and Yuv: What are they?
RGB and RGBA The formats are somewhat clear and widely used in computer graphics, so we will not dive into the depths of the basics. In short, when your computer prepares an image, it works with three channels –Red (P)for Green (g)And Blue (b). This is how most screens work.
RGBA Additional channel adds – Alpha (a) It controls transparency, which makes it especially useful for web graphics and digital design. RGB It accurately represents colors without distortion, but it has a critical defect – it takes a lot of space. For example, an accurate image 1920 × 1080
in RGBA Coordination (using a byte 1 per channel) takes: 1920×1080×4 = 8294400 bytes ≈ 8.2 MB
Compressed formats such as JPEG reduce the size of the file, but in the world of P2P video flow and actual time processing on customer machines-such as identifying objects, discovering the key point, and retail-this is not an applicable option. We need to transfer and analyze each frame in the actual time, without entering the artifacts of pressure or losing important details. This is the place Yop He plays his role in playing, and offers a more intelligent approach to a balance between quality, efficiency and performance.
What is yuv?
Unlike RGB, which stores color information directly, Yop It separates a picture to Loma (Y) Chroma components (U and V). This approach allows effective data pressure without losing great quality.
P (Loma, brightness) It represents the brightness of the pixels, and determines the extent of the appearance of light or darkness. Basically, this is the gray version (in white and black) of the image, while preserving all shapes and details.
U and V (chroma, color) Store color information but less precisely because the human eye sees brightness more severe than the color of color. Simply put, these channels act as a “transformation” of two -dimensional brightness towards the various forms of colors.
This chapter is the key to the Yuv video pressure, broadcasting, and video -based video processing.
Why is Yuv better for video flow?
One Yop The less clear, but very effective advantages is that one of its channels (Y) It is not intended to store the color at all. Instead, he describes precisely
How is this related to human vision?
Human eye imagines pictures using two types of light receptors in the retina:
-
Code cells (about 120 million) – Step for error and contrast, but unable to discover the color. It allows us to see shapes and details even in low light.
-
Conical cells (about 6 million) – Responsible for color perception, but the number is less than 20 times. It only works in good lighting conditions and comes in three types: red, green and blue (RGBIt is not surprising).
Because of this imbalance in the future, our brains give priority to the color. If brightness or contrast is distorted, we notice it immediately. However, transformations often pass in color without anyone noticing them.
This is the primary principle of Yuv
- the Y The channel (brightness) is still unchanged to preserve the forms of objects so that the rod cells in your eyes are happy.
- the U and Fifth Channels (color information) can be compressed without creating noticeable artifacts visually, and you will not notice fewer cone cells, that is, the difference of difference.
This means that it is unlike RGB – When the three channels are equally important – YUV treats its channels differently based on human awareness. Since color data (U and V) Less important, we can reduce the amount of data sent without the concrete quality loss.
This is exactly the way the CHROMA sampling mechanism works – improving video coding by selectively compressing color information while maintaining brightness properly.
How to save Chroma samples world flow world
Chroma Subsampling It is a technique to reduce the amount of color data in the image. Instead of storing the color per pixel (as in RGBYuram reduces the accuracy of color channels while maintaining brightness (shape).
There are many industry standards for the sub -names of Chroma:
-
4: 2: 2 Sub -sam – Each pair of pixels shares color information. The eye hardly notices the difference, but the file size is 33 % reduced. This method is rarely used.
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4: 2: 0 sub – – The color is stored for only one pixel out of four, to achieve maximum pressure.
Why 4: 2: 0 The main criterion?
This format cuts the data volume into two halves without significantly degrading image quality. This is why it is the mobile standard for almost all broadcasting and video services. For example, the Microsoft teams transmit the video to 4: 2: 0 It provides the best balance between quality and the efficiency of the frequency range.
In this preparation, one color is four pixels, and the human eye does not discover the difference – even when zooming – during brightness (Y) remains unchanged.
1920×1080×1.5 = 3110400 bytes ≈ 3.1 MB
On one frame, this results in more than just a double decrease in the volume of data compared to RGBA – Without any clear loss of quality!
The image below shows how the frame/final image looks with 4: 2: 0 Chroma Subsampling. Notice how one U He describes four Y4 times the memory win!
Why Yuv is very useful for Amnesty International?
In today’s world, Amnesty International Video Programs are quickly expanding. Nerve networks are not used not only to analyze the surveillance camera and enhance the quality of the flow but also for the most complex tasks such as obstetric effects, actual time adjustment, identification of organisms and movement tracking.
For example, we have developed a virtual makeup system that applies lipstick and eye samples on the person’s face in a video conversation – as it is realisticly moved as possible. In such tasks, the accuracy in shape and movement is very important, while color information is secondary. You can also train your model for understanding Greyscale to enhance their performance, at the same time, get Greyscale photos on a more efficient graphics processing unit if you take Yue 4: 2: 0 As an entrance, since you only need to cut the first part of the image to get the resulting Greyscale channel.
The main challenges in the flow of artificial intelligence video
Things are more than color
Artificial intelligence models, such as many other computer vision systems, focus mainly on the structure of the object, shape and edges instead of cloning the exact colors. This is true to recognize face, track tracking, detect anomalies, and AR effects. For example, in the movement recognition system, the outline of the pixels of the body is much more important than the color of the skin.
Performance is very important
For artificial intelligence in actual time, each frame must be addressed under 20 ms
To maintain a smooth frame rate (50–60 FPS
). The fastest the nerve network receives and tires to treat it, the greater the natural and fluids that the application works.
- RGB Very heavy formats – a
1920×1080
Weighs RGBA framework8.2 MB
Huge pressure on memory and processing strength. - Yop With 4: 2: 0 The Chroma sub -sam is reduced from unnecessary data in
O(1)
By transferring the color with less accuracy, providing mathematical resources without losing visible quality.
Treating GPU improved
Modern graphics processing units are significantly improved for YUV processing, which means that we can work with images without converting them to RGB. This eliminates unnecessary calculations and enhances treatment speed.
Danger display and memory provision
Reducing data volume is necessary to transmit video and process it in actual time:
- In broadcasting, using Yop 4: 2: 0 cuts the data transfer by 50 % without noticeable quality loss.
- In artificial intelligence, models can process compressed data without amplifying them RGBSave VRAM and mathematical power.
conclusion
Let’s be honest – RGB appears to be the clear choice. It is the standard in cameras, screens and computer graphics. But when it comes to broadcasting video in the real world and the integration of artificial intelligence, RGB It turns into Slow dinosaurs. then Yop Steps to the episode, which provides an ideal balance for quality, speed and data efficiency. The smart storage system (separating brightness from compressed color) enables things that would be a calculation nightmare in RGB.
- Less data = more speed. No one wants an additional megapixel slow video processing.
- The eye does not notice the trick. Our brain focuses on the shape, not the loss of simple colors – Yop Fullly benefit from this.
- Artificial intelligence is concerned with artistic fangs, not colored nuances. When you only have 16 millimeters per frame, YuV eliminates unnecessary accounts and provides resources.
- GPUS I love Yuv. Armed coding programs for devices, fast accounts, and minimal format transfers-everything you need for high-performance video.
The final ruling
RGB Wonderful-but not where the performance is involved in actual time and AI. In video flow, Yop It is the real spine and it operates main solutions for years.
So, if you still think RGB is king, then it is time to rethink. Video format has been running long ago through their own rules.