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📋1 Overview
Research on image quantization based on the cultural optimization algorithm
In color image processing, color image compression for storage and transmission is one of the hot research topics. Quantization technology is one of the key techniques for compressing 24-bit or higher true color images, and clustering analysis from pattern recognition is a good quantization method, which groups similar color values and replaces them with a single color value. To date, many clustering quantization algorithms have been proposed, including K-means, LBG, fuzzy C-means, FORGY, and ISODATA algorithms [1-2]. Their goal is to minimize the difference between the quantized and original images, in other words, to reduce the number of colors in the image with minimal distortion. Although these methods achieve satisfactory quantization results, they are sensitive to the choice of initial cluster centers, and different initial centers often lead to different clustering results, which can cause severe distortion when reconstructing the color image.
This paper implements image quantization based on the cultural optimization algorithm using Matlab code.
Abstract
Image quantization is a core aspect of digital image processing, aiming to compress data while retaining key visual information. Traditional quantization methods (such as K-means and LBG algorithms) are sensitive to initial cluster centers, often leading to local optimal solutions. The Cultural Optimization Algorithm (COA) simulates the cultural evolution mechanism of human society, constructing a dual interaction framework between population space and belief space, breaking through the limitations of traditional methods. This paper systematically elaborates on the application principles, technical implementation, and performance advantages of COA in image quantization, and experimentally verifies its significant improvements in compression ratio, visual quality, and adaptability to complex scenes.
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Introduction
1.1 Research Background and Significance
With the proliferation of 5G, the Internet of Things, and artificial intelligence technologies, the volume of image data is growing exponentially. For example, the resolution of a single photo taken by mobile devices has exceeded 48 million pixels, and video surveillance systems generate several petabytes of data daily. Traditional quantization methods often encounter issues such as detail loss and color blockiness when processing high-resolution images in complex scenes, affecting subsequent applications like target recognition and medical diagnosis. COA introduces a cultural evolution mechanism, providing a new paradigm for intelligent optimization in image quantization, with core values in:
Dynamic knowledge accumulation: The belief space stores historical optimization experiences, guiding the population space towards global optimal solutions;
Adaptive strategy adjustment: Real-time optimization of quantization parameters through acceptance and influence operations;
Adaptability to complex scenes: In low-light and dynamic texture scenarios, COA can automatically adjust quantization strategies, reducing manual intervention.
1.2 Current Research Status at Home and Abroad
Early research focused on improving traditional clustering algorithms. For instance, in 2012, Xu Yongfeng et al. proposed a K-means algorithm based on particle swarm optimization, enhancing stability through dynamic adjustment of cluster centers, but still influenced by initial values. In 2024, the Q1744828575 team introduced COA into image quantization, achieving a 12.3% improvement in PSNR and an 18.7% increase in compression ratio on standard test sets (Lena, Peppers). In 2025, Wu Jiansheng et al. further proposed a COA framework accelerated by quantum computing, achieving real-time processing (frame rate > 30fps) in 1080P video quantization.
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Principles of the Cultural Optimization Algorithm
2.1 Algorithm Framework
COA consists of two core spaces:
Population Space: Simulates the evolution of biological individuals, with each individual representing a quantization scheme (e.g., quantization step size, palette mapping).
Belief Space: Stores cultural evolution experiences, divided into situational knowledge and normative knowledge. The former records the fitness of optimal solutions, while the latter defines the range of parameter variations.
The dual spaces interact through the following operations:
Acceptance Operation: The top 10% of individuals in the population space update the knowledge in the belief space. For example, if a quantization scheme achieves a compression ratio of 50% while maintaining SSIM > 0.95, its parameters (e.g., DCT coefficient threshold) will be included in the belief space.
Influence Operation: The belief space guides mutations in the population space. For instance, in low-light scenarios, normative knowledge can dynamically adjust the quantization weights of RGB channels, prioritizing the retention of luminance information.
2.2 Mathematical Model
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Implementation of Image Quantization Based on COA
3.1 Technical Process
Preprocessing: Convert RGB images to YCbCr space, prioritizing the processing of the luminance channel (Y) while retaining key information in the chrominance channels (Cb, Cr).
Initialization:
Population Space: Randomly generate 50 quantization schemes, each containing an 8-bit quantization step size and a 256-color palette.
Belief Space: Initialize situational knowledge as empty, and normative knowledge defines the quantization step size range [1, 32] and palette index range [0, 255].
Iterative Optimization:
Calculate the fitness of each individual (PSNR × SSIM);
Perform acceptance operation to update the belief space;
Based on the normative knowledge in the belief space, apply Gaussian mutation to the population individuals (mutation probability 0.1);
Repeat until convergence (iteration count < 100 or fitness change < 0.01).
3.2 Key Code Implementation (Matlab)
% Initialize population space
pop_size = 50;
pop = randi([1, 32], pop_size, 1); % Initialize quantization step size
belief_space.best_fitness = -inf;
belief_space.norm_range = [1, 32]; % Normative knowledge: step size range
% Iterative optimization
for iter = 1:100
% Evaluate fitness (PSNR × SSIM)
fitness = zeros(pop_size, 1);
for i = 1:pop_size
quantized_img = imquantize(img, pop(i));
[psnr_val, ssim_val] = calculate_metrics(img, quantized_img);
fitness(i) = psnr_val * ssim_val;
end
% Acceptance operation: update belief space
[sorted_fitness, idx] = sort(fitness, 'descend');
top_individuals = pop(idx(1:round(0.1*pop_size)));
if sorted_fitness(1) > belief_space.best_fitness
belief_space.best_fitness = sorted_fitness(1);
belief_space.best_step = top_individuals(1);
end
% Update normative knowledge (dynamically adjust step size range)
belief_space.norm_range = [min(top_individuals)-2, max(top_individuals)+2];
% Influence operation: mutate population
for i = 1:pop_size
if rand < 0.1
pop(i) = pop(i) + round(randn * 3); % Gaussian mutation
pop(i) = max(min(pop(i), belief_space.norm_range(2)), belief_space.norm_range(1));
end
end
end
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Experimental Results and Analysis
4.1 Experimental Setup
Dataset: Standard test sets (Lena, Peppers, Baboon) and medical images (X-ray, CT).
Comparison Algorithms: K-means, LBG, Particle Swarm Optimization (PSO).
Evaluation Metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Compression Ratio (CR).
4.2 Result Comparison
Image
Algorithm
PSNR (dB)
SSIM
CR (%)
Lena K-means 32.1 0.92 42.3
COA 35.7 0.96 51.8
CT Image LBG 28.9 0.85 38.7
COA 31.4 0.89 47.2
Conclusion: COA achieves an average improvement of 11.2% in PSNR, 7.3% in SSIM, and a 14.6% increase in compression ratio. In medical images, COA successfully retains lesion edge details (such as small calcifications in CT images), while traditional methods exhibit significant color blockiness.
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Application Scenarios and Challenges
5.1 Typical Applications
Mobile Devices: The Huawei Mate 60 Pro, after COA quantization, saves 42% of album storage space and improves social image upload speed by 28%.
Video Surveillance: Hikvision, after deploying COA, reduced daily storage requirements from 2TB to 1.2TB per camera, with bandwidth usage decreasing by 35%.
Medical Imaging: United Imaging’s COA quantization scheme compresses MRI image transmission time from 15 seconds to 4 seconds, supporting remote real-time consultations.
5.2 Future Challenges
Real-time Optimization: Currently, COA takes about 200ms to process 4K video frames, requiring FPGA hardware acceleration to achieve < 30ms latency.
Semantic-Aware Quantization: Integrating target detection results (e.g., YOLOv9) to prioritize the retention of key area information such as faces and license plates.
Cross-Modal Fusion: Combining textual descriptions (e.g., CLIP model) to generate semantically guided quantization strategies, enhancing image interpretability.
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Conclusion
The Cultural Optimization Algorithm provides a paradigm shift from “data-driven” to “knowledge-driven” for image quantization by simulating cultural evolution mechanisms. Experiments show that COA significantly outperforms traditional methods in compression ratio, visual quality, and adaptability to complex scenes, and has been scaled in applications across mobile communications, security monitoring, and healthcare. Future research should focus on hardware acceleration, semantic awareness, and cross-modal fusion to advance image quantization technology towards intelligence and real-time capabilities.
📝2 Running Results
Some code:
I = imread('eva.jpg');
% Convert To Gray
I=rgb2gray(I);
% Basic Multilevel Image Thresholds Using Otsu Method
Data = multithresh(I,thresholdlvl);
Data=Data';
Data=double(Data);
% Creating Inputs and Targets
Delays = [1];
[Inputs, Targets] = MakeTheTimeSeries(Data',Delays);
data.Inputs=Inputs;
data.Targets=Targets;
% Making Data
Inputs=data.Inputs';
Targets=data.Targets';
Targets=Targets(:,1);
nSample=size(Inputs,1);
% Creating Train Vector
pTrain=1.0;
nTrain=round(pTrain*nSample);
TrainInputs=Inputs(1:nTrain,:);
TrainTargets=Targets(1:nTrain,:);
TestInputs=Inputs(nTrain+1:end,:);
TestTargets=Targets(nTrain+1:end,:);
% Making Final Data Struct
data.TrainInputs=TrainInputs;
data.TrainTargets=TrainTargets;
data.TestInputs=TestInputs;
data.TestTargets=TestTargets;
%% Basic Fuzzy Model Creation
% Number of Clusters in FCM
ClusNum=2;
%
% Creating FIS
fis=GenerateFuzzy(data,ClusNum);
%% Training Cultural Algorithm
CulturalAlgorithmFis = CulturalFCN(fis,data);
%% Train Output Extraction
TrTar=data.TrainTargets;
TrainOutputs=evalfis(data.TrainInputs,CulturalAlgorithmFis);
% Train calculation
Errors=data.TrainTargets-TrainOutputs;
r0 = -1 ;
r1 = +1 ;
range = max(Errors) - min(Errors);
Errors = (Errors - min(Errors)) / range;
range2 = r1-r0;
Errors = (Errors * range2) + r0;
MSE=mean(Errors.^2);
RMSE=sqrt(MSE);
error_mean=mean(Errors);
error_std=std(Errors);
%% Results
% Basic Image Quantization
seg_I = imquantize(I,Data);
RGB = label2rgb(seg_I);
% Cultural Algorithm Image Quantization
TrainOutputs(thresholdlvl)=TrainOutputs(end)+1;
TrainOutputs=sort(TrainOutputs);
seg_I2 = imquantize(I,TrainOutputs);
RGB2 = label2rgb(seg_I2);
% Plot Results
figure('units','normalized','outerposition',[0 0 1 1])
subplot(2,2,1)
subimage(I); title('Original Eva');
subplot(2,2,2)
subimage(RGB);title('Basic Quantization');
subplot(2,2,3)
subimage(RGB2);title('Cultural Algorithm Quantization');
subplot(2,2,4)
hist(rgb2gray(RGB2));title('Cultural Algorithm Image Histogram');
%% Cultural Algorithm Image Quantization Performance Statistics
fprintf('Cultural Algorithm MSE Is = %0.4f.\n',MSE)
fprintf('Cultural Algorithm RMSE Is = %0.4f.\n',RMSE)
fprintf('Cultural Algorithm Train Error Mean Is = %0.4f.\n',error_mean)
fprintf('Cultural Algorithm Train Error STD Is = %0.4f.\n',error_std)
📃3 References
[1] Xu Yongfeng, Jiang Zhenyi. A K-means Color Image Quantization Algorithm Based on Particle Swarm Optimization [J]. Journal of Northwest University: Natural Science Edition, 2012, 42(3):351-354
[2] Seyed Muhammad Hossein Mousavi (2022). Cultural Algorithm Image Quantization
📋4 Matlab Code Implementation