With the rapid iteration of Mini/Micro LED technology and the increasing segmentation of display scenarios, the image quality and cost control of LED displays have become the core focus of industry competition. Among these, real pixels, virtual pixels, and pixel sharing technology are the three pillars determining the core performance of a display, directly impacting the product's resolution, color reproduction, power consumption, and overall cost. This article will start from the technical essence, combining cutting-edge industry practices and test data to provide a comprehensive and in-depth analysis of these three technologies, offering industry professionals a complete reference system from technical principles to application scenarios.
Real Pixel Technology: The "Picture Quality Benchmark" Constructed by Physically Emitting Units Real pixel technology is the most basic and core display solution for LED displays. Its essence is to directly construct images through physically existing LED beads (sub-pixels). Each pixel unit has independent brightness and color control capabilities, and it is the "benchmark standard" for measuring picture quality accuracy in the industry.
Definition and Core Features
The core definition of a real pixel is a "physically visible independent light-emitting unit," meaning that each pixel on the display screen is composed of one or more LED beads (usually red (R), green (G), and blue (B) primary color sub-pixels), and each pixel unit achieves current regulation through an independent driving channel, without any "virtual dots" generated by algorithmic interpolation. 1. Pixel Composition: The mainstream real pixel unit adopts a "1R1G1B" three-primary-color sub-pixel combination (some high-end screens use "2R1G1B" to enhance the red color gamut). The sub-pixel packaging forms are mainly SMD and COB, with COB packaging becoming the mainstream choice for small-pitch real pixel screens due to its smaller LED bead spacing. 2. Key Parameter Definitions:
Ø Pixel Spacing (P-value): Refers to the distance between the centers of two adjacent physical pixels (unit: mm). For example, P2.5 indicates a pixel center spacing of 2.5mm, which is a core indicator for measuring pixel density.
Ø Pixel Density: The calculation formula is "1/(P-value × 10^-3)^2" (unit: dots/m²). For example, the pixel density of P2.5 is 1/(0.0025)^2 = 160,000 dots/m², directly determining the image's detail.
Ø Grayscale Levels: Real pixels support 16-bit (65,536 levels) to 24-bit (16,777,216 levels) grayscale. Higher grayscale levels result in smoother color transitions, without "color blocks" or "blurring" phenomena, which is crucial for high-precision scenarios such as medical imaging and surveillance. 1.2 In-depth Analysis of Technical Principles The working principle of real pixels is based on "independent driving + three-primary-color mixing". The core logic is to precisely control the current of each sub-pixel through the driver IC to adjust the ratio of the RGB three primary colors, ultimately synthesizing the desired color and brightness. 1. Independent Driving Architecture: The driving system of a real pixel screen adopts a "one-to-one" channel design, meaning that each sub-pixel (R/G/B) corresponds to an independent constant current channel of the driver IC. The current adjustment range is typically 1-20mA (normal scenarios) or 20-50mA (high-brightness scenarios, such as outdoor screens). This architecture ensures that the brightness deviation of each sub-pixel can be controlled within ±3%, and the brightness uniformity far exceeds that of virtual pixel solutions. 2. Three-Primary-Color Mixing Mechanism: Based on the characteristics of human vision, real pixels achieve coverage of different color gamut standards (such as sRGB, DCI-P3, Rec.709, etc.) by adjusting the current ratio of the R/G/B sub-pixels. For example, under the DCI-P3 cinematic color gamut requirements, real pixels need to increase the current ratio of green subpixels to 50%-60% (the human eye is most sensitive to green), red to 25%-30%, and blue to 15%-20%. Virtual pixels, relying on interpolation, cannot achieve such precise ratio control.
3. Advantage of no interpolation: Real pixels do not require any software algorithm interpolation; the image is directly composed of physical pixels. Therefore, there is no "ghosting" or "blurring" in dynamic images. The dynamic response speed depends only on the switching speed of the driver IC (typically 50-100ns), far faster than the millisecond-level response of virtual pixels.
1.3 Typical Application Scenarios and Selection Logic Due to its "high stability and high precision" characteristics, real-pixel technology is mainly used in scenarios with stringent image quality requirements and no room for cost compromise. Specific selection should consider three dimensions: viewing distance, display content, and industry standards:
High-Precision Professional Scenarios:
Ø Command Center Dispatch: Requires 24/7 uninterrupted operation, MTBF (Mean Time Between Failures) ≥ 50,000 hours, and no motion blur in dynamic images. Typically, a P0.7-P1.25 real-pixel screen is selected.
2. Close-Range Viewing Scenarios:
Ø Conference Rooms/Lecture Halls: Viewing distance is typically 2-5 meters. Text (such as PPT documents) needs to be clear and free of jagged edges. A P1.25-P2.5 real-pixel screen is selected.
Ø Museum Display Cases: Requires reproduction of artifact details (such as calligraphy, paintings, and bronze textures). Viewing distance is 1-3 meters. A P1.25-P1.8 real-pixel screen is selected. 1.4 Performance Advantages and Technical Limitations
1.4.1 Core Advantages
Ø Top-tier image quality stability: No algorithm interpolation dependency, no distortion in static/dynamic images, brightness uniformity ≤ ±5% (COB packaging ≤ ±3%), color reproduction ≥ 95% (sRGB), setting an industry benchmark for image quality;
Ø High long-term operational reliability: Independent driver architecture reduces the impact of single IC failure on the overall image, and eliminates the "algorithm aging" problem of virtual pixels (such as decreased interpolation accuracy after long-term operation);
Ø Adaptable to high dynamic range content: Supports dynamic frame rates ≥ 60fps, and refresh rates can easily reach 7680Hz (meeting the needs of professional camera shooting), with no ghosting in fast-moving scenes (such as live racing broadcasts). 1.4.2 Major Limitations
Ø High Cost Control Difficulty: The core cost of real-pixel displays comes from "LED chips + driver IC + receiver card". Taking a 100㎡ display as an example, the number of LED chips used in a P1.2 real-pixel screen is 1/(0.0012)^2×100≈69,444,444 (approximately 69.44 million chips), which is 4.3 times that of a P2.5 real-pixel screen (16 million chips). Assuming a cost of 0.1 yuan per LED chip, the cost difference is 5.34 million yuan. Simultaneously, the P1.2 screen requires more driving channels (32 driving IC channels per square meter, compared to only 16 channels for P2.5), and the number of receiver cards used is also doubled, resulting in a comprehensive cost that is 2.5-3 times that of P2.5.
Ø Physical Pixel Density Limited by Packaging: Currently, the minimum real-pixel pitch for SMD packaging is P0.9, and COB packaging can reach P0.4. However, smaller pitches (such as below P0.3) are limited by the size of the LED chip, making further breakthroughs difficult. Ø Relatively high power consumption: Due to the high density of LED beads, the power consumption of a real pixel screen is usually 30%-50% higher than that of a virtual pixel screen, which places higher demands on the power supply system of large outdoor screens.
Virtual Pixel Technology: A Cost-Image Quality Balance Achieved Through Algorithm Interpolation
Virtual pixel technology is an innovative solution created to address the pain points of "high cost and low density" of physical pixels. Its core is to generate virtual light-emitting points in the gaps between physical pixels through software algorithms, thereby improving visual resolution without increasing the number of physical LEDs. It is the preferred technology for "cost-effectiveness first" in low-to-mid-range scenarios.
2.1 Definition and Core Characteristics The core definition of virtual pixels is "algorithm-generated visual virtual points." This means that some pixels on a display screen are not composed of physical LEDs, but rather "trick" the brain by superimposing the brightness of adjacent physical pixels and alternating their time, utilizing the characteristics of human vision to create a "higher resolution" visual perception.
Ø Technical Essence: Virtual pixels do not change the number or arrangement of physical pixels; they only optimize the visual effect through algorithms. Therefore, there is a difference between their "actual resolution" (physical pixel density) and "visual resolution" (virtual pixel density). For example, a P2.5 physical pixel screen can achieve a "visual P1.25" effect through virtual technology, but the actual physical density is still 160,000 dots/m².
Ø Core Classification: Based on different implementation methods, virtual pixels are divided into two main categories: "spatial virtual" and "temporal virtual." Currently, "spatial virtual" is the mainstream in the industry (accounting for over 80%). Temporal virtual, due to its high hardware requirements, is only used in high-end virtual screens (such as small studios). 2.2 In-depth analysis of technical principles The working principle of virtual pixels is based on "visual illusion + algorithm interpolation". Virtual points are generated through two core paths. The technical logic and image quality performance of different paths are significantly different.
2.2.1 Spatial Virtual Technology (Mainstream Solution) Spatial virtual technology utilizes the "brightness mixing of adjacent physical pixels" to generate virtual points between physical pixels. The core is to calculate the brightness weights of adjacent pixels using algorithms to achieve color synthesis of virtual points. 1. Typical Solution: RGBG Four-Light Virtual Arrangement (Most Widely Used in the Industry) Traditional physical pixels are arranged in a uniform "RGB-RGB" pattern, while the RGBG virtual solution changes the arrangement to "RGB-G-RGB-G," that is, adding one green sub-pixel between every two RGB physical pixels, forming a "1R1G1B+1G" unit structure. At this point, the algorithm combines the R and B sub-pixels of two adjacent physical pixels with the middle G sub-pixel to generate four virtual pixels (as shown in the figure below): a. Virtual pixel 1: Composed of the R, G, and B of physical pixel A (basic real pixel); b. Virtual pixel 2: Composed of the R of physical pixel A, the middle G, and the B of physical pixel B (interpolated virtual point); c. Virtual pixel 3: Composed of the R of physical pixel B, the middle G, and the B of physical pixel A (interpolated virtual point); d. Virtual pixel 4: Composed of the R, G, and B of physical pixel B (basic real pixel); In this way, the theoretical resolution can be improved by 2 times (some manufacturers claim 4 times, but in reality, it is a 2-fold increase in visual resolution, while the physical resolution remains unchanged), and due to the addition of the green sub-pixel, the perceived brightness is improved by 15%-20% (consistent with the characteristics of human vision). 2. Interpolation Algorithm Types: The image quality of spatial virtualization depends on the accuracy of the interpolation algorithm. Currently, the mainstream algorithms are divided into two categories: a. Bilinear Interpolation: Calculates the average brightness of 4 adjacent physical pixels to generate virtual points. The algorithm is simple and computationally inexpensive, but the edges are blurry (text strokes are prone to "fuzzy edges"); b. Bicubic Interpolation: Calculates the brightness weights of 16 adjacent physical pixels to generate virtual points. The image quality is more delicate (edge blur is reduced by 40%), but it requires a more powerful main control chip, increasing the cost by 10%-15%.
2.2.2 Temporal Virtualization Technology (High-End Solution) Temporal virtualization utilizes the "persistence of vision" effect of the human eye. By rapidly switching the brightness of different physical pixels, virtual points are generated by superimposing them in the time dimension. The core is "frame splitting + high-frequency refresh". Ø Technical Logic: A complete frame of image is divided into N "sub-images" (typically N=4-8). Each sub-image only illuminates a portion of the physical pixels. These sub-images are rapidly alternated through a high-frequency refresh rate (≥3840Hz) on the display. Due to visual persistence, the human eye perceives these sub-images as a single "high-resolution" frame. For example, when N=6, a frame is divided into 6 sub-images, each illuminating a different area of physical pixels, ultimately resulting in 35 virtual pixels (far exceeding the 4 virtual pixels in spatial representation).
Ø Hardware Requirements: Time-based virtualization requires a display supporting a refresh rate of ≥7640Hz (to meet the shooting requirements of 60fps dynamic scenes and prevent the camera from capturing sub-image transitions), and the driver IC must have "fast current switching" capability; otherwise, "flickering" or "alternating brightness" phenomena will occur.
2.3 Typical Application Scenarios and Selection Logic The core advantages of virtual pixel technology are "low cost and high visual resolution." Therefore, it is mainly used in scenarios where "viewing is at a medium to long distance, cost is sensitive, and text precision requirements are not high." The selection should focus on the "match between viewing distance and visual resolution":
Medium to long distance advertising scenarios:
Ø Shopping mall atrium/outdoor advertising screens: The viewing distance is usually 5-15 meters. Extreme detail is not required, and cost control is necessary. A P2.5-P3.9 spatial virtual screen is selected (e.g., a 50㎡ atrium screen in a shopping mall uses a P2.5 RGBG virtual solution, with a visual resolution equivalent to P1.25. At a distance of 8 meters, the image quality is close to that of a P1.5 real pixel screen, but the cost is reduced by 40%, and the number of LED beads is reduced from 8 million to 6 million). Ø Large screens in transportation hubs (such as high-speed rail stations and airports): Viewing distance is 10-20 meters. Large text (such as "Ticket Gate A1") and dynamic videos need to be displayed. P3.9-P5.0 virtual screens are selected (a 300㎡ P4.8 virtual screen in a high-speed rail station with a refresh rate of 3840Hz, at a distance of 15 meters, the text clarity meets the recognition requirements, and the cost is 1.2 million yuan cheaper than real pixel screens). 2. Cost-Sensitive Entertainment Scenarios: Ø KTV Rooms/Bars: Require high-saturation colors (such as red and blue) to create atmosphere; viewing distance 3-5 meters; low text precision requirements (only song titles and lyrics); P2.5-P3.0 virtual screens are recommended (a KTV chain uses P2.5 virtual screens; each room is 5㎡, saving 3000 yuan compared to solid pixel screens, and the algorithm increases red brightness by 20%, meeting the visual needs of entertainment scenarios); Ø Small Studios (Non-Professional): Require "high visual resolution" to improve image quality; limited budget; P2.0 time-based virtual screens are recommended (a local TV station's 15㎡ P2.0 time-based virtual screen, refresh rate 7680Hz, visual resolution equivalent to P1.0, meeting shooting needs within 10 meters, costing 60% less than P1.0 solid pixel screens). 3. Temporary Setup Scenarios: Ø Large Screens for Exhibitions/Events: Short usage period (1-3 days), requiring rapid deployment and controllable costs. P3.9-P5.9 virtual screens are selected (a 200㎡ P4.8 virtual screen at an exhibition had a rental cost of only 50% of a real pixel screen, and setup time was reduced by 30%. Due to viewing distances exceeding 8 meters, there was no significant difference in image quality).
Performance Advantages and Technical Limitations
2.4.1 Core Advantages
Ø Significant Cost Advantage: At the same visual resolution, virtual pixel screens use 30%-50% fewer LEDs than real pixel screens (RGBG solution reduces LED usage by 25%, time-based virtual solution by 50%), and the number of driver ICs and receiver cards is reduced by 20%-40%. Taking a 100㎡ screen with a P1.25 visual resolution as an example, the overall cost of a virtual screen (physical P2.5) is approximately 800,000 yuan, while that of a physical pixel screen (P1.25) is approximately 1.5 million yuan, representing a 47% cost reduction.
Ø Flexible and adjustable visual resolution: The virtual pixel density can be adjusted according to scene requirements through algorithms. For example, a P2.5 physical screen can be switched to "visual P1.25" or "visual P1.67" to adapt to different viewing distances (e.g., in shopping malls, P1.25 visual resolution is used during the day when the viewing distance is far; at night, when the viewing distance is close, P1.67 is switched to avoid blurring).
Ø Lower power consumption: Due to the reduced number of LEDs, the power consumption of a virtual pixel screen is typically 30%-40% lower than that of a physical pixel screen with the same visual resolution, making it suitable for long-term operation of large outdoor screens. 2.4.2 Main Limitations
Ø Dynamic images are prone to blurring: Due to the reliance on interpolation between adjacent pixels, the brightness update of virtual points lags behind that of physical pixels in dynamic images (such as 60fps video), easily resulting in "ghosting" (test data shows that the ghosting length of the P2.5 virtual screen at 60fps is about 0.8 pixels, while that of the physical pixel screen is only 0.1 pixels); although time-based virtualization can improve this, it requires a refresh rate of ≥7640Hz, increasing the cost by 20%;
Ø Insufficient text display precision: The text edges of virtual pixels are generated by interpolation, lacking the "hard edges" of physical pixels, leading to a decrease in text clarity. Actual testing shows that the clarity of text displayed on the P2.5 virtual screen at a distance of 2 meters is only equivalent to that of a P4.8 real-pixel screen (text strokes appear jagged, and small fonts ≤12 are difficult to read), which is unsuitable for close-range text-based office scenarios;
Ø Color gamut and brightness uniformity deviation: Although the spatial virtual RGBG arrangement increases green sub-pixels, the spacing between red and blue sub-pixels increases, resulting in color uniformity deviation that is 1-2 times higher than that of a real-pixel screen; during time-based virtual factor image switching, brightness fluctuations can reach ±10%, easily causing "flickering" (especially in low-brightness scenarios);
Ø Dependence on algorithm and hardware matching: The image quality of virtual pixels is highly dependent on the collaboration of "interpolation algorithm + driver IC + main control chip," otherwise the algorithm cannot run in real time, resulting in "lag"; if the driver IC switching speed is insufficient (e.g., >100ns), time-based virtual images will overlap, severely degrading image quality.
Pixel Sharing Technology: A "Precise Optimization Solution" Through Hardware and Algorithm Collaboration
Pixel sharing technology is a "compromise solution" between real and virtual pixels. Its core is to allow multiple virtual pixels to reuse the driving channel and light-emitting unit of the same physical pixel through hardware arrangement optimization and software algorithm upgrades. This maximizes cost reduction while maintaining a certain image quality, making it the "optimal solution" for small-size, high-information-density scenarios.
3.1 Definition and Core Features
The core definition of pixel sharing is "physical pixel reuse + algorithm optimization." This means increasing the number of key sub-pixels (such as green) by changing the arrangement of LEDs (hardware level), while simultaneously using algorithms to allow multiple virtual pixels to share the driving resources of the same physical pixel (such as current channels and IC pins), achieving the dual goals of "resolution improvement + cost control." Ø Technical Essence: Pixel sharing is not simply a "virtual pixel upgrade," but a combination of "hardware reconstruction + algorithm iteration"-changing the sub-pixel arrangement at the hardware level (e.g., RGB→RGBG→RGGB), and optimizing the brightness weight and edge sharpening of virtual points at the algorithm level, ultimately achieving "better image quality than virtual pixels and lower cost than real pixels."
Ø Core Difference: Compared to virtual pixels, pixel sharing's "reuse" is "hardware-level reuse" (rather than simple algorithm interpolation). For example, in an RGBG arrangement, the middle green sub-pixel not only serves adjacent physical pixels but also provides brightness support for 2-3 virtual pixels, sharing the same driving channel and reducing IC usage. Compared to real pixels, pixel sharing still has virtual points, but through hardware arrangement optimization, the brightness deviation between virtual and physical points can be controlled within ±5% (virtual pixels are typically ±10%).
In-depth Analysis of Technical Principles
The working principle of pixel sharing consists of two main modules: "hardware arrangement reconstruction" and "software algorithm optimization," which work together to achieve a balance between image quality and cost. 3.2.1 Hardware Arrangement Reconstruction (Core Foundation) The core of the hardware level is "optimizing subpixel arrangement and increasing the density of key subpixels". By changing the traditional uniform RGB arrangement, the density of the color that the human eye is sensitive to (green) is increased, while the number of driving channels is reduced. Specifically, there are two mainstream solutions: 1. RGBG Arrangement Scheme (most widely used): The traditional "RGB-RGB" arrangement is changed to "RGB-G-RGB-G", that is, an independent green subpixel is added between every two RGB physical pixel units to form a repeating unit of "1R1G1B+1G". At this point, the central green sub-pixel not only belongs to its own physical unit but also provides green brightness support for the virtual pixels of the two RGB units on the left and right (i.e., "1 G sub-pixel serves 3 pixel units"), realizing hardware reuse of the green sub-pixel; simultaneously, the driving channel is designed as "independent R/B channels, shared G channels", meaning that 2 RGB units share 1 G driving channel, reducing the G channel usage of the driver IC by 50% (e.g., in a 100㎡ P2.5 RGBG screen, the G channel usage is reduced from 2.28 million real pixels to 1.14 million). 2. RGGB Arrangement Scheme (High-end Solution): The arrangement is further optimized to "RG-GB-RG-GB", meaning each unit contains "1R1G" and "1G1B", increasing the green sub-pixel density to twice that of red/blue (the R/G/B density is the same in real pixels). This arrangement better matches the human eye's sensitivity to green, improving color reproduction by 10%-15% compared to RGBG (approaching the level of real pixels). Simultaneously, it boasts a higher driving channel reuse rate-every four virtual pixels share one G channel, reducing IC usage by 25% compared to the RGBG solution.
3.2.2 Software Algorithm Optimization (Image Quality Assurance) The core of the pixel sharing algorithm is "eliminating virtual point deviation and improving text clarity." It addresses the inherent pain points of virtual pixels through three key algorithms: 1. Average Display Algorithm (Representative Manufacturer: Carlette): This algorithm performs a "weighted average calculation" on the brightness of the physical pixels surrounding each virtual pixel, controlling the brightness deviation between virtual and physical points within ±3%. For example, when displaying text, the algorithm identifies virtual points at the text edges and increases their brightness weight (5%-8% higher than physical points) to offset edge blur. Actual testing shows that at a distance of 1.5 meters, the text clarity of a P2.0 pixel-sharing screen is equivalent to a P2.5 real pixel screen (traditional virtual pixels are only equivalent to P4.0); 2. Dynamic Contrast Algorithm (Representative Manufacturer: Nova): Analyzes image content in real time, reducing the brightness of virtual dots in dark areas and increasing the brightness of virtual dots in bright areas to enhance image contrast. For example, when displaying text on a dark background, the algorithm reduces the brightness of background virtual dots while increasing the brightness of text virtual dots, making the text "stand out" and preventing it from blending into the background.
3. Subpixel Compensation Algorithm: Addressing the issue of large R/B subpixel spacing in RGBG/RGGB arrangements, the algorithm reduces color deviation through "brightness compensation of adjacent R/B subpixels." For example, when displaying red areas, the algorithm increases the brightness of R subpixels in adjacent physical pixels, filling in the "color gaps" caused by excessive R subpixel spacing, making the red area more uniform.
Typical Application Scenarios and Selection Logic
Pixel sharing technology, due to its characteristics of "good small-size adaptability, high information density, and controllable cost," is mainly applied to scenarios with "small to medium sizes, close-range viewing, and certain requirements for text accuracy." Selection should consider "screen size, display content, and power consumption requirements."
1. Small and Medium-Sized Commercial Display Scenarios: Ø Mobile Phone Store Display Screens: Screen size is typically 3-8㎡, viewing distance 1-3 meters. It needs to display phone specifications (small font) and product images. A P2.0-P2.5 pixel shared screen is recommended (a mobile phone brand store uses a 5㎡ P2.0 RGGB pixel shared screen, which increases information density by 40% compared to a P2.5 pixel screen of the same size, and can simultaneously display specifications for 8 mobile phones; text remains clear and unblurred at a distance of 1.5 meters).
Ø Convenience Store Advertising Screens: Size 1-3㎡, viewing distance 2-5 meters. It needs to display product prices (small font) and promotional information. A P2.5-P3.0 pixel shared screen is recommended (a chain convenience store uses 1000 2㎡ P2.5 pixel shared screens, which are 35% cheaper and consume 40% less power than a pixel screen, suitable for 24-hour operation). 2. Indoor Information Display Scenarios: Ø Bank Queue Display: Size 1-2㎡, viewing distance 3-5 meters, needs to display the queue number (large font) and service prompts (small font), using a P2.0-P2.5 pixel shared screen (a bank branch uses a 1.5㎡ P2.0 pixel shared screen, the queue number is clearly visible at a distance of 5 meters, and the small font service prompts can be recognized at a distance of 3 meters, saving 25% in cost compared to a solid pixel screen). 3. Low-power consumption scenarios: Ø Outdoor small-size screens (e.g., bus stop screens): Size 2-5㎡, requires solar power, power consumption ≤100W/㎡, using P2.5-P3.9 pixel shared screens (100 3㎡ P3.0 pixel shared screens at a bus stop in a certain city consume 80W/㎡, 50% lower than real pixel screens, and can be completely powered by solar energy without external power grid); 3.4 Performance advantages and technical limitations 3.4.1 Core advantages Ø Optimal balance between cost and image quality: The cost of pixel sharing is 40%-60% lower than that of real pixels (100㎡ P2.0 pixel shared screen costs about 600,000 yuan, while real pixel screen costs about 1 million yuan), and the image quality is 30%-50% better than virtual pixels (text clarity is equivalent to a real pixel screen with a physical P value 0.5 smaller than its own, such as P2.0 pixel sharing being equivalent to P2.5 real pixels), making it the "king of cost-effectiveness" for small and medium-sized scenarios; Ø High Information Density: Through hardware arrangement optimization, the sub-pixel density of pixel sharing (especially green) is 25%-50% higher than that of virtual pixels, resulting in stronger information carrying capacity. For example, a 5㎡ P2.0 pixel sharing screen can display 12 lines of text (25 characters per line), while a P2.0 virtual screen of the same size only displays 8 lines (20 characters per line), increasing information density by 87.5%;
Ø Good Hardware Compatibility: Pixel sharing does not require special high-end main control chips; conventional main control chips can support it, and it is compatible with both SMD and COB packages (COB-packaged pixel sharing screens have better brightness uniformity, ≤±4%), adapting to different scenario requirements;
Ø Balanced Power Consumption and Reliability: The number of LEDs used is 30%-40% less than that of real pixels, and power consumption is 30%-50% lower than that of real pixels. At the same time, due to the high reuse rate of drive channels, the number of ICs is reduced, resulting in a failure rate 20% lower than that of virtual pixel screens. 3.4.2 Main Limitations
Ø Dependence on specific hardware arrangement: The core of pixel sharing is hardware arrangement (such as RGBG/RGGB). Traditional RGB arrangement displays cannot achieve pixel sharing through software upgrades, requiring redesign of the PCB board and LED mounting process, leading to increased customization costs.
Ø Poor adaptability to large-size scenarios: Pixel sharing algorithm optimization is mainly for small-size screens (<10㎡). For large-size screens (>10㎡), due to the large number of physical pixels, the computational load of the algorithm increases exponentially, easily resulting in "stuttering" or "uneven image quality".
Ø Dynamic response limited by IC: The virtual pixels of pixel sharing depend on the driving channels of physical pixels. If the switching speed of the driving IC is insufficient, the brightness update of virtual points in dynamic images will lag, resulting in "ghosting".
Ø Color gamut upper limit is lower than that of real pixels: Although pixel sharing adds green sub-pixels, the spacing of R/B sub-pixels is still larger than that of real pixels, resulting in a slightly lower color gamut coverage (sRGB coverage is about 92%, while real pixel screens are about 98%), which cannot meet the color gamut requirements of professional images (such as post-processing of photography).
4.2 Scenario-Based Selection Guide
1. Scenarios Prioritizing Real-Pixel Pixels:
Ø Core Requirements: High precision, high stability, long-term operation;
Ø Typical Scenarios: Medical imaging (DICOM standard), command centers (7x24 operation), museum artifact display (close-up detail);
Ø Selection Recommendations: P0.9-P2.5, COB packaging (small pitch) or SMD packaging (medium pitch), grayscale level ≥16bit, refresh rate ≥3840Hz.
2. Scenarios Prioritizing Virtual-Pixel Pixels:
Ø Core Requirements: Low cost, medium to long distance, visual resolution;
Ø Typical Scenarios: Shopping mall atrium advertising, outdoor large screens, temporary exhibition setups;
Ø Selection Recommendations: P2.5-P5.9, spatial virtual (RGBG) or temporal virtual (high-end), refresh rate ≥3840Hz (to avoid shooting flicker), bicubic interpolation algorithm.
3. Prioritize Pixel Sharing Scenarios: Ø Core Requirements: Small to medium size, close-range text, cost balance; Ø Typical Scenarios: Mobile phone store display cases, elevator information screens, convenience store advertising; Ø Selection Recommendations: P1.8-P2.5, RGBG/RGGB arrangement, algorithm supports average display + dynamic contrast, driver IC switching speed ≤100ns.
V. Industry Technology Development Trends
With the maturity of Mini LED technology and the commercialization of Micro LED, three major technologies are constantly iterating and upgrading:
1. Real Pixel Technology: Developing towards "smaller pitch and higher integration." Currently, COB packaged real pixels have achieved P0.4. In the future, P0.2 or lower can be achieved through Micro LED chips (size <50μm). Combined with AI image quality optimization algorithms (such as dynamic color gamut adjustment), the image quality performance in professional scenarios will be further improved;
2. Virtual pixel technology: Developing towards "temporal-spatial fusion virtualization," it reduces dynamic ghosting to within 0.3 pixels through a hybrid algorithm of "spatial interpolation + temporal alternation." Combined with Mini LED backlighting technology, it improves brightness uniformity (≤±6%), adapting to more mid-to-high-end scenarios.
3. Pixel sharing technology: Developing towards "multi-subpixel reuse," it will expand RGBG to "RGBWG" (adding white subpixels) in the future, further improving brightness. Simultaneously, through AI real-time rendering algorithms, it solves the problem of uneven image quality on large-size screens, adapting to medium-sized scenarios of 10-50㎡.
In summary, real pixels, virtual pixels, and pixel sharing technologies are not "substitutes," but rather "complementary solutions" for different scenarios. It is necessary to select the most suitable technology solution from three dimensions: "scenario requirements, cost budget, and long-term operation and maintenance," in order to maximize commercial value while ensuring image quality.
