CC BY
0APPLICATION OF YUV COLOR MODEL IN REAL-TIME
IMAGE PROCESSING
1Bozorov A.A., 2Shoykulov Sh.K.
1Lecturer, department of Engineering and technical support of security, University of Public
Security, Tashkent, Republic of Uzbekistan 2Acting Associate Professor, Department of Applied Mathematics, Karshi State University,
Karshi, Uzbekistan https://doi.org/10.5281/zenodo.14030668
Abstract. This article discusses the use of the YUV color model for real-time image processing. This model, due to its ability to separate brightness and color information, is widely used in video compression tasks, as well as in video surveillance systems, video conferencing and other real-time applications. The advantages and disadvantages of YUV compared to other color models, such as RGB, are considered.
The article describes subsampling and filtering methods, and provides examples of its effective application in various fields, including video systems and media platforms. Particular attention is paid to the possibilities of its use in virtual and augmented reality.
Keywords: YUV color model, image processing, real time, video compression, video surveillance, video conferencing, RGB, filtering, subsampling, VR, AR.
INTRODUCTION
Real-time image processing is becoming increasingly important due to its widespread use in areas such as video surveillance, broadcasting, telemedicine, and virtual reality. Color models play an important role in this process, allowing for the efficient transmission and processing of visual data. Some of the most common models are RGB and YUV.
The RGB (Red, Green, Blue) color model is based on a combination of three primary colors that are mixed to form any other shade. However, this model is not always optimal for real-time image processing, as it requires significant computing resources.
The YUV model, in turn, divides image information into brightness (Y) and color difference components (U and V). This allows for a reduction in data volume and more efficient use of compression without significant deterioration in image quality. This is especially important for video streams and broadcasts, where the amount of information is critical for processing speed [1,4].
The purpose of this article is to analyze and evaluate the possibilities of using the YUV color model for real-time image processing tasks, taking into account its advantages over the traditional RGB model.
RESULTS and DISCUSSIONS
The YUV color model is one of the key models in the field of image and video processing, especially in television systems and video coding. It was developed to separate the image into components that are better suited to human perception and more efficient for data transmission and processing.
The YUV model divides the image into three components:
Y (brightness or luminance) - describes the light output and affects the perception of the brightness of the image, which is an important aspect, since human vision is more sensitive to changes in brightness.
U and V (color difference components) - transmit color information. U encodes the difference between blue and brightness, and V - the difference between red and brightness. These components allow you to store color data and use less memory to transmit color information, which slightly affects visual perception.
The conversion from RGB to YUV can be expressed by the following equations:
Y=0.299-R+0.587-G+0.114-B U=0.492-(B-Y) V=0.877-(R-Y)
Here R, G, and B are the components of the RGB model, and Y, U, and V are the corresponding components of the YUV model.
The main advantage of YUV is the reduction in the amount of transmitted data. Human vision is more sensitive to brightness changes, which allows storing color information at a lower resolution without visible loss. This provides more efficient data compression [2,3].
In addition, color-separated data allows the use of more advanced compression algorithms (for example, JPEG or MPEG), which helps to reduce the amount of transmitted data without significant loss of image quality.
The YUV color model is actively used in video coding, television broadcasts and video streaming. It plays a key role in optimizing data transmission in areas where it is important to maintain high image quality with minimal transmission costs, such as television broadcasting, video surveillance and other digital video technologies.
Thus, the YUV model provides an optimal balance between the efficiency of data storage and transmission, as well as image quality, which makes it in demand in modern video processing and broadcasting systems.
The YUV color model is actively used in real-time image processing tasks due to its efficiency in data compression and the ability to transmit high-quality visual materials with minimal losses. Important areas of its application are video coding, digital television broadcasting, video surveillance, as well as virtual and augmented reality applications.
Let's consider the main tasks of the YUV color model:
Video coding and compression
The YUV color model is widely used in video compression algorithms such as H.264 and HEVC to optimize the storage and transmission of visual data. By separating the brightness and color components, there is a significant reduction in the amount of information transmitted, which allows maintaining high image quality at low network bandwidth.
Digital television broadcasting and streaming video
In digital television broadcasting and video streaming, YUV ensures efficient transmission of video streams with minimal costs for storing and transmitting data. Video content is transmitted with optimized color information, which can significantly reduce the load on data transmission channels and improve the quality of video on user devices.
Video surveillance systems
Video surveillance using the YUV color model allows you to optimize streaming video in real time, preserving detailed images while reducing the load on communication channels. This is
especially important for systems that work with a large number of cameras and require continuous data transmission.
Video Processing for VR/AR
Virtual and Augmented Reality applications require high-speed graphics processing and minimal data transfer latency. Using YUV allows to significantly reduce the amount of data transferred without losing quality, which is critical for ensuring realistic interaction in virtual environments.
Video Conferencing
In video conferencing systems, YUV is used to reduce the amount of data transferred, which helps improve video quality at low network bandwidth. This ensures the stability of video calls and high performance even with unstable Internet connections.
Let's look at some examples of scenarios for using the YUV color model:
Mobile devices. The YUV color model is used on mobile devices to compress and process multimedia content, which allows you to play video and support video calls with less load on the processor and memory.
Interactive applications and games. In video games and interactive applications, using YUV for rendering textures and video can improve performance and reduce latency in graphics processing.
Analytics. Automatic camera data analysis systems use YUV to efficiently process and transmit video streams, reducing computational costs and improving real-time performance. The use of the YUV color model in real time allows for a significant reduction in data volumes and high-quality visualization, making it a key technology for many modern applications.
There are various methods used to process images using the YUV color model that effectively separate the brightness and color components, thereby optimizing the image processing process. Among these methods are:
Conversion from RGB to YUV. The first step in processing images in the YUV model is the conversion from RGB. This linear transformation separates the brightness (Y) and color (U and V) information, which allows for more efficient compression and processing of the data. Conversion formulas allow you to determine how brightness and color are derived from the original RGB values, which is important for various applications such as image compression and video coding [5].
Filtering and noise reduction. The YUV model allows you to apply filters to the brightness (Y) and color (U and V) information separately, which helps minimize noise, especially in the brightness component. More aggressive filters can be used for color components, since the eye is less sensitive to color changes, which improves the overall image quality while maintaining high detail.
Data Compression Algorithms. In image and video compression algorithms such as JPEG and MPEG, YUV is used to reduce the amount of data. Luminance information is processed with higher precision, while color data can be reduced in resolution, which allows for a smaller file size without significant loss of image quality.
Color Correction and White Balance. Separating luminance from color information in the YUV model greatly simplifies tasks such as color correction and white balancing. Color manipulation is performed through the U and V components, which allows for color information to be adjusted without affecting the image brightness.
Scaling and Interpolation. Scaling images in YUV is simplified by focusing on the Y (luminance) component, which is responsible for detailed display. This reduces the number of calculations when working with color information and speeds up the scaling process.
Object and Edge Detection. Object and edge detection in images can be performed efficiently using only the Y luminance component. This reduces computational costs and improves the accuracy of algorithms such as edge detectors that work better with luminance changes than with color.
Video Compression Algorithms. Video compression algorithms such as H.264 and HEVC apply down sampling to the U and V color components to reduce data volume without sacrificing image quality. This makes YUV an ideal model for streaming video and digital television.
These techniques demonstrate how the YUV color model is used to optimize image compression, filtering, analysis, and correction, making it a preferred choice for real-time image processing and video transmission.
The YUV color model is widely used in commercial and industrial systems due to its efficiency in processing images and video streams, especially in situations where it is necessary to optimize data transmission and reduce the load on systems. Let's consider the main scenarios of its use:
Television broadcasting. The first application of the YUV model was associated with color television. In this system, brightness information is transmitted with high accuracy, while color data is transmitted with reduced resolution. This helps to reduce the amount of data required to transmit a television signal while maintaining image quality.
Video coding. Video codecs such as H.264 and H.265 make extensive use of YUV for video compression. By reducing the resolution of the U and V color components, the overall amount of data is reduced, which makes it possible to stream video on platforms such as YouTube and Netflix without significant loss of quality [6].
Digital photography and image processing. In photography, the YUV model is used to compress images in the JPEG format. By reducing the resolution of color components, it is possible to reduce the file size without significant loss of quality, which is used in programs such as Adobe Photoshop for editing and storing images.
Video surveillance systems. In video surveillance systems, YUV is used for efficient video processing and storage. Cameras and recording systems use YUV-based video compression to reduce the amount of data transmitted over the network and minimize resource costs without compromising video quality.
Video conferencing and streaming. Video communication programs such as Zoom or Skype use YUV to optimize video streams. By reducing the resolution of color data, programs reduce the amount of information transmitted, which helps stabilize video communication even at low Internet speeds.
Virtual and augmented reality. In VR and AR technologies, YUV is used to optimize video streams and process graphic information in real time, which reduces the load on systems, improving graphics quality and overall performance.
Telecommunications. In video telephony systems such as WhatsApp and FaceTime, YUV is used to compress video streams, which allows minimizing traffic while maintaining acceptable video quality.
These examples show that the use of YUV is an important element for improving the efficiency of image and video stream processing in various industries.
The YUV color model, along with other models, has its own unique advantages and limitations. To better understand its features, it is useful to conduct a detailed comparison with models such as RGB, HSV, and CMYK, which are also widely used in various areas of image processing.
Comparison of YUV and RGB. In RGB, each color is formed by mixing the red, green, and blue channels with the same degree of accuracy. However, RGB is less efficient in terms of compression, since all three channels (R, G, B) require transmission with the same data density. In YUV, the components are divided into a luminance channel (Y) and two color channels (U and V). This allows for efficient data reduction by compressing color channels, which is an important advantage when encoding video and transmitting images in real time.
Comparison of YUV and HSV. HSV (Hue, Saturation, Value) is more intuitive for humans, as it is based on color perception, making it useful for editing and color correction. However, it is less efficient in terms of real-time data transmission, as its structure is not adapted for compression. YUV, on the contrary, is better suited for video, where saving traffic and resources is important.
Comparison of YUV and CMYK. CMYK is a subtractive color model used in the printing industry. Unlike YUV, which is focused on digital images and video, CMYK is used in printing-related tasks. The main difference is in the approach to color rendering. YUV works with brightness and colors separately, which makes it more suitable for dynamic images. YUV has a number of advantages for video processing and real-time data transmission compared to other color models. It is best suited for applications where compression and quality preservation with minimal resources are important.
Optimization of YUV image processing plays an important role in improving the performance and efficiency of systems, especially in real time. This model is widely used in video coding and other resource-constrained tasks. The main optimization methods include the following:
Color Component Compression. One of the key advantages of YUV is the ability to compress color channels (U and V) without significant loss of image quality. The human eye is less sensitive to color details compared to brightness, so the resolution of color channels can be reduced, which reduces the amount of data to transmit or store.
Hardware Acceleration. Modern devices such as GPUs and application-specific integrated circuits (ASICs) support acceleration of YUV image operations. This can significantly speed up image processing and reduce the load on central processors. Software must be adapted to take advantage of such capabilities.
Buffering and Caching. Efficient use of memory is achieved by buffering data, which reduces the number of accesses to slower storage devices, and caching temporary data speeds up recalculations when processing successive frames.
Chroma Subsampling. Chroma subsampling is widely used to reduce the amount of data. For example, the popular 4:2:0 format preserves the full resolution of the luminance channel, while the color components are at a reduced resolution, which significantly reduces the amount of data transmitted.
Parallel Processing. Using multithreading and parallel processing of parts of the image on multi-core processors can significantly speed up computations. However, it is important to properly manage threads and memory resources to prevent conflicts.
Optimizing Compression Algorithms. Compression algorithms such as H.264 and H.265 work effectively with YUV, using motion prediction and other techniques to reduce the amount of data. Optimizing such algorithms for specific systems can improve performance and video quality [7].
These optimization methods can significantly reduce the amount of data and speed up the processing of YUV images while maintaining high image quality.
The use of the YUV color model in real time is accompanied by a number of difficulties and limitations that can affect the efficiency and quality of image processing.
Quality loss due to compression. One of the main drawbacks of YUV is the need to compress the U and V color components to reduce the data size. Chroma subsampling (e.g. 4:2:0) reduces the resolution of the color channels, which leads to artifacts and image quality degradation, especially on highly textured and contrasted details.
Color space limitations. YUV has a smaller color gamut compared to models such as RGB and Lab. This limitation can negatively impact color accuracy in applications that require a wide range of colors, such as image processing for artistic and design purposes.
High computational costs. Despite the data reduction, YUV requires additional computational resources to convert to RGB before displaying on the screen, since most displays work in RGB format. This can complicate tasks in systems where fast processing and minimal latency are critical.
Hardware limitations. Not all devices have native support for YUV. Many platforms are designed to natively support RGB, which requires conversion between color spaces, which can reduce performance.
Difficulties with display on different screens. Since many displays use RGB, converting YUV to RGB can cause color deviations, especially when displayed on devices with different screen characteristics, such as monitors, TVs, and mobile devices.
Artifacts in dynamic scenes. Scenes with fast movement in video can suffer from noticeable artifacts due to heavy compression or insufficient resolution of color components. This is especially critical in video games or sports broadcasts, where high detail is important with fast frame rates.
Using the YUV color model in real time requires finding a compromise between image quality and computational efficiency, as well as applying optimization techniques to help compensate for possible limitations of the color model.
The YUV color model continues to evolve, especially with the growth of multimedia usage and the increase in data volumes transmitted over the Internet. Let's consider the prospects for its further development and application.
Growing role in streaming video and virtual reality (VR). With the growing popularity of streaming platforms and virtual reality technologies, YUV remains the main model for video compression due to its ability to reduce the amount of data. New video formats such as 8K and 12K require efficient compression with minimal quality loss, and YUV remains one of the best solutions for this. In virtual reality, where high frame rates are important, the use of YUV allows for more efficient encoding and transmission of images.
Optimization for AI and machine learning. The use of YUV in machine learning and artificial intelligence (AI) tasks for processing images and video streams opens up new possibilities. Neural networks can work more efficiently with data in the YUV format, as it is optimized for human perception. In the future, there will be machine learning algorithms specifically designed to work with YUV, which will improve video encoding and decoding.
Improving compression for IoT and mobile devices. With the rapid development of the Internet of Things (IoT) and mobile technologies, there is a growing need for efficient methods of transmitting multimedia data. YUV, due to its ability to reduce the amount of data, is ideal for low-power devices such as smart cameras and sensors that transmit video streams. The use of YUV will ensure high data transfer rates and optimized power consumption in such devices.
Use in 8K TVs and new displays. With the development of display technologies such as 8K and even higher resolutions, the need for data compression models increases. YUV remains the standard for high-definition broadcasts, providing high-quality video compression and transmission.
Application in virtual and augmented reality (AR/VR). With the growing popularity of virtual and augmented reality technologies, YUV continues to be used in real-time visualization tasks. With efficient compression and minimal latency, YUV enables high-quality rendering in such systems.
YUV will play an important role in the future as video coding, VR/AR, and streaming services evolve, continuing to optimize image processing and improve algorithms for widespread use.
CONCLUSIONS
The processed YUV color model is of great importance in the field of real-world image and video stream processing, especially in cases where high data compression with minimal quality loss is required. In digital television systems, streaming video services and video conferencing, YUV helps to significantly reduce the amount of transmitted information. By separating the brightness and color components, the model reduces the load on communication channels without degrading the visual characteristics.
However, the implementation of YUV is not without difficulties. For example, with high color accuracy in tasks such as medical imaging, slight distortions in color reproduction are possible. Also, the complexity of conversion between YUV and other color models, such as RGB, can cause additional time and resource costs in real time.
The prospects for using YUV in the future are associated with the development of new data compression methods, increasing video resolution (for example, with the transition to 8K) and the implementation of virtual reality (VR) and augmented reality (AR) technologies. Improved hardware support and data processing algorithms make it possible to expand the boundaries of YUV use in real time.
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