Project Overview
Interactive web application demonstrating Singular Value Decomposition for image compression
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The Challenge
Traditional image compression methods often lack transparency in how they work, making it difficult for students and researchers to understand the underlying mathematical principles of image compression.
- Complex mathematical concepts
- Limited interactive learning tools
- Performance bottlenecks in processing
- Lack of real-time compression control
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The Solution
An interactive web application that demonstrates SVD-based image compression in real-time, allowing users to explore the relationship between compression ratio and image quality through an intuitive interface.
- Real-time SVD computation
- Interactive compression control
- Visual quality comparison
- Educational insights
Method & Implementation
Advanced mathematical algorithms with optimized web performance
SVD Algorithm Implementation
Mathematical Foundation
- Singular Value Decomposition
- Matrix factorization: A = UΣV^T
- Per-channel RGB processing
- Rank-k approximation
Optimization Techniques
- WebAssembly for computation
- Parallel processing
- Memory-efficient algorithms
- Lazy loading strategies
Frontend Architecture
Next.js 14 Features
- App Router architecture
- Server and client components
- TypeScript integration
- Optimized bundling
User Interface
- Drag & drop file upload
- Real-time compression slider
- Side-by-side comparison
- Performance metrics display
Performance Optimization
Web Performance
- Cumulative Layout Shift (CLS) = 0
- Lighthouse score: 90/73/100
- Optimized image loading
- Efficient memory management
Processing Speed
- 45% faster processing
- WebAssembly acceleration
- Asynchronous operations
- Progressive enhancement
Results & Performance
Exceptional performance metrics and user experience improvements
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Performance Metrics
0
CLS Score
90/73/100
Lighthouse
20MB+
Upload Size
45%
Faster Processing
Compression Quality
File Size ReductionUp to 80%
Quality Preservation90% at 50% compression
Processing SpeedReal-time
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Technical Achievements
Algorithm Efficiency
- Optimized SVD computation
- Memory-efficient processing
- Parallel channel processing
User Experience
- Intuitive interface design
- Real-time feedback
- Educational value
Future Enhancements
- Video compression support
- Advanced algorithms
- Mobile app version