A hardware and software linkage control method and system for image adaptive quantization-based percussion bead discretization and color mapping optimization

Abstract

This invention discloses a hardware-software integrated control method and system for discretization and color mapping optimization of Perler beads based on adaptive image quantization, relating to the fields of digital image processing and intelligent manufacturing control. The method includes: preprocessing the input image and converting it to a unified color space; performing adaptive resolution quantization based on the physical size of the Perler beads and target grid parameters to discretize the continuous image into a Perler bead array; constructing a standard color library for Perler beads and optimizing color mapping by combining comprehensive color difference calculation and a visual perception weight model; reducing local distortion caused by quantization errors through an error diffusion mechanism; generating a Perler bead arrangement matrix and converting it into equipment control commands to achieve automatic arrangement and printing of the Perler beads. This invention effectively improves image restoration accuracy and color consistency by introducing adaptive quantization and perception optimization mechanisms. Simultaneously, by combining hardware-software integrated control, it enhances the automation level and production efficiency of Perler bead production, demonstrating good practical value and promising prospects for widespread application.

Description

Technical Field

[0001] This invention relates to the fields of digital image processing technology, intelligent manufacturing control technology, and creative graphic generation technology. Specifically, it relates to a hardware and software linkage control method and system based on image adaptive quantization for Perler beads discretization and color mapping optimization, which is applicable to the automatic generation and arrangement of Perler beads patterns and an integrated printing output terminal. Background Technology

[0002] Perler bead patterns are typically created using a grid system, arranging and combining different colored beads to represent image content. Current technologies for converting images into Perler bead patterns primarily rely on fixed-resolution scaling and finite color table matching methods, whose core limitations are as follows: 1. The lack of an adaptive quantization mechanism makes it impossible to dynamically adjust the resolution and grain scale according to the content of the input image, resulting in loss of detail or redundancy; 2. The color mapping process does not consider the differences in human visual perception and is based solely on simple distance calculations, which easily leads to comprehensive color bias. 3. The software processing is disconnected from the hardware execution, making it impossible to directly drive the bead arrangement device or printing module; 4. The actual color library, inventory constraints, and layout feasibility of the Perler beads were not optimized. 5. Lack of error compensation mechanism, resulting in significant cumulative deviations in large-sized patterns.

[0003] Therefore, it is necessary to propose an integrated method that takes into account image processing accuracy, color reproduction capability, and device feasibility. Summary of the Invention

[0004] The purpose of this invention is to provide a hardware and software linkage control method and system for image adaptive quantization-based bead discretization and color mapping optimization. By introducing a multi-scale adaptive quantization model, a perception-driven color matching mechanism, and a device-oriented arrangement control strategy, high-fidelity conversion and automated output of images to bead arrays are achieved. Technical solution

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a hardware and software linkage control method for image adaptive quantization-based bead discretization and color mapping optimization, comprising the following steps.

[0006] Step 1: Image Preprocessing The input image is denoised, converted to a different color space, and normalized to a different size. The image is converted from the RGB color space to the perceptually uniform Lab color space, and a multi-scale image representation is constructed.

[0007] Step 2: Adaptive Quantization Modeling Based on the local texture complexity and edge information of the image, an adaptive quantization function is constructed to dynamically determine the resolution of the bead grid, so that the high-frequency region maintains a high resolution and the low-frequency region is compressed.

[0008] Step 3: Discretization Mesh Generation Based on the quantization results, a corresponding two-dimensional array grid of bead-shaped pixels is generated, and a mapping relationship between pixel blocks and bead-shaped pixels is established.

[0009] Step 4: Color Mapping Optimization A color library model for Perler beads is constructed, candidate colors are calculated based on the color difference formula, and a visual weight function and neighborhood consistency constraint are introduced to perform global optimization allocation of Perler bead colors.

[0010] Step 5: Error Propagation and Compensation An improved error propagation algorithm is adopted to propagate the quantization error in the neighborhood, while combining the bead arrangement rules for error limitation and boundary constraint compensation.

[0011] Step 6: Layout and Path Planning Based on the generated perlite array, a partitioned filling strategy is used to generate the arrangement path, which improves the efficiency of automatic arrangement and reduces mechanical repetitive movements.

[0012] Step 7: Hardware and software linkage control The arrangement result is converted into control commands and sent to the bead arrangement device and printing module to realize automatic pattern output of bead beads.

[0013] Step 8: Results Feedback and Correction The layout results are detected by sensors or vision acquisition modules. If there is a deviation, it is fed back to the system for local correction.

[0014] Compared with the prior art, the present invention has the following advantages: 1. Significantly improves image detail preservation through adaptive quantization mechanism; 2. Color mapping based on visual perception effectively reduces overall color difference and improves the color reproduction of Perler beads; 3. Achieve deep integration between software algorithms and hardware devices to improve the level of automation; 4. Introduce error propagation and compensation mechanisms to ensure the overall consistency of large-size patterns; 5. Supports different color libraries and device structures, with good scalability and adaptability. Attached Figure Description

[0015] The attached diagram is described below: Figure 1 is a schematic diagram of the overall process of the method of the present invention; Figure 2 is a schematic diagram of the adaptive quantization model; Figure 3 is a flowchart of color mapping optimization; Figure 4 is a schematic diagram of the perlite bead layout path planning; Figure 5 is a flowchart of the hardware and software linkage control system. Figure 6 A Perler Bead Discretization System for Adaptive Image Quantization 1 Figure 7 A monochrome highlighting system for adaptive quantization color optimization of images 1 Figure 8 Image Adaptive Quantization Color Optimization Monochrome Highlighting System 2 Figure 9 Image Adaptive Quantization Color Optimization Monochrome Highlighting System 3 Figure 10 Image Adaptive Quantization of the Perler Bead Discretization System 2 Figure 11 Hardware and software linkage structure diagram Detailed Implementation

[0016] In practice, users upload target images via terminals. The system first performs preprocessing and multi-scale analysis, then generates a perlite-resolution grid using an adaptive quantization model. Subsequently, the color mapping module is invoked to match image colors to the perlite color library, and the overall visual effect is optimized through an error diffusion algorithm. The system includes an image processing module, a color mapping module, a layout optimization module, and a hardware control module.

[0017] During the placement phase, the system generates control commands based on the bead array and sends them to the bead placement device via a communication interface for automatic placement. Simultaneously, the printing module outputs reference templates or auxiliary positioning information. Finally, a vision inspection module verifies the bead placement result and automatically corrects local deviations. An image processing module, running on a host computer or embedded system, performs image preprocessing and quantification analysis. A color mapping module performs optimal matching calculations based on a preset bead color library (e.g., containing 96 standard colors). A placement optimization module generates execution paths based on the device structure, such as line-by-line scanning or partitioning strategies. The hardware control module controls the actuators via a microcontroller or industrial controller.

[0018] This method can be widely applied in automatic bead-laying machines, creative DIY equipment, and educational smart terminals. For example, in one specific embodiment: the user inputs a photo of a person, and the system automatically adjusts it to fit the resolution of a 104×104 bead-laying board; the main colors are extracted using a clustering algorithm and mapped to 96 standard colors for the beads; an error diffusion algorithm is used to optimize the detail areas; after generating the bead-laying matrix, path planning reduces the number of color switching times by approximately 30%; finally, the device automatically completes the bead placement and outputs a reference image for user confirmation.

[0019] This method can also be combined with machine learning models to train on historical Perler bead data to further optimize color mapping and arrangement strategies. This invention achieves fully automated control from image input to finished Perler bead output by deeply integrating an image adaptive quantization algorithm with the hardware system for Perler bead production, demonstrating high practical value and promising prospects for wider application.

Claims

1. A hardware and software linkage control method and system for image adaptive quantization-based perlite discretization and color mapping optimization, characterized in that, Includes the following steps: (1) Obtain the input image to be processed and preprocess the input image, including size normalization, noise suppression and color space conversion; (2) Based on the physical size of the beads and the grid parameters of the target bead board, perform adaptive resolution quantization processing on the input image to generate a discrete grid image consistent with the bead array; (3) Construct a bead color library and map the colors of the input image to the bead color library, and optimize color matching by combining color difference calculation and visual perception weight model; (4) Perform error diffusion processing on the quantized image to reduce the accumulation of local color differences generated during the color mapping process; (5) Generate the arrangement matrix data of the perlite beads, and generate equipment control instructions based on the arrangement matrix; (6) Send the control command to the automatic arrangement and printing output module of the perlite pattern to realize the automatic arrangement and auxiliary printing output of the perlite pattern.

2. The method according to claim 1, characterized in that, The color space conversion in step (1) adopts the RGB to LAB conversion method to enhance the accuracy of color distance calculation.

3. The method according to claim 1, characterized in that, The adaptive resolution quantization process in step (2) includes: dynamically adjusting the grid size according to the complexity of the image content, and determining the quantization density of local areas through edge detection and texture analysis.

4. The method according to claim 1, characterized in that, The bead arrangement matrix mentioned in step (3) includes the color number and spatial coordinate information of each grid position.

5. The method according to claim 1, characterized in that, It also includes a path optimization step, which optimizes the movement path of the automatic bead arrangement device to reduce execution time.

6. The method according to claim 1, characterized in that, The printing output module is used to output reference templates for Perler beads patterns or positioning auxiliary marks.

7. A perler bead pattern generation and automatic arrangement system, characterized in that, include: An image processing module, a color mapping module, a control generation module, and an execution system, wherein the system is used to perform the method according to any one of claims 1 to 9.

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