An image processing system that reduces an image's color palette while preserving visual quality.
Median Cut produces a fast initial colour reduction which K-Means then refines, while Sobel and Canny edge detection protects fine detail during quantization. Each pixel is mapped to its nearest optimised palette colour and the result is benchmarked with MSE, PSNR and SSIM.
Naive colour quantization destroys edges and gradients, producing visible banding. Pure clustering is accurate but slow; pure Median Cut is fast but crude.
Combine the two — Median Cut for speed, K-Means for accuracy — and add explicit edge preservation so detail survives the palette reduction, then prove the gain with standard image quality metrics.
Median Cut + K-Means Hybrid Clustering
Fast initial color reduction refined by clustering optimization.
Edge Preservation
Sobel/Canny-based edge detection to protect fine image details during quantization.
Adaptive Color Assignment
Maps each pixel to the nearest optimized palette color.
Quality Evaluation
Benchmarked using MSE, PSNR, and SSIM metrics.
Image processing
Machine Learning
Evaluation
Image input
The source image is loaded and converted to a working colour space.
Initial reduction
Median Cut produces a fast candidate palette.
Refinement
K-Means optimises the palette for minimal distortion.
Edge-aware mapping
Pixels are assigned while preserving detected edges.
Benchmarking
Output quality is measured against the original.
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Yes. Share your guide's requirements, preferred technology stack and abstract format, and we adapt the modules, dataset or UI accordingly.
That is the point of the explanation session. We walk you through the architecture, every module, the flow of data and the results, and hand over a viva question bank with answers.
We help you with setup end to end — dependencies, environment, database and configuration — over chat or a call until it runs on your machine.
Yes. Post-delivery support is included, so you can come back for fixes, doubts or demo help even after the project is delivered.
Get complete source code, documentation, PPT, explanation, and support.
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