A serverless image processing pipeline built on AWS that automatically optimizes and resizes high-resolution images for web use.

This project provides an automated solution for image optimization, perfect for CMS systems, portfolios, blogs, and e-commerce platforms. Upload high-resolution images and receive optimized versions in multiple resolutions (1080p, 720p, 480p) with 90-98% file size reduction.

• Automated Processing: Upload triggers automatic resizing and compression
• Multiple Resolutions: Generates 1080p, 720p, and 480p versions
• High Compression: 90-98% file size reduction with minimal quality loss
• Fast Processing: 0.6-6 seconds depending on image size
• Secure: Pre-signed URLs, no exposed AWS credentials
• Professional UI: Modern dark theme with drag-and-drop upload
• Cost-Effective: $0 cost (within AWS Free Tier)

http://image-pipeline-frontend-anshuman.s3-website.ap-south-1.amazonaws.com/

• Compression: 90-98% file size reduction
• Processing Time: 0.6-6 seconds
• Quality: Minimal visible degradation
• Formats Supported: JPEG, PNG, BMP, TIFF, GIF

• HTML5, CSS3, JavaScript (Vanilla)
• S3 Static Website Hosting

• AWS Lambda (Python 3.11)
• Pillow (PIL) for image processing
• Boto3 (AWS SDK)

• Amazon S3 (Storage)
• AWS API Gateway (REST API)
• AWS IAM (Security)
• AWS CloudWatch (Monitoring)

image-optimization-pipeline/
├── frontend/                    # Web application
│   ├── index.html              # Main HTML
│   ├── styles.css              # Professional dark theme
│   └── app.js                  # Frontend logic
├── lambda-functions/           # AWS Lambda functions
│   ├── image-processor/        # Main processing Lambda
│   ├── generate-upload-url/    # Pre-signed URL generator
│   └── get-processed-images/   # Download URL generator
├── docs/                       # Documentation
│   ├── architecture/           # System design
│   ├── implementation/         # Implementation details
│   └── testing/                # Test results
└── scripts/                    # Deployment scripts

• AWS Account
• AWS CLI configured
• Python 3.11

1. Clone the repository

git clone https://github.com/yourusername/image-optimization-pipeline.git
cd image-optimization-pipeline

2. Deploy Lambda Functions

# Deploy image processor
cd lambda-functions/image-processor
zip -r function.zip .
aws lambda update-function-code --function-name ImageProcessorLambda --zip-file fileb://function.zip

# Deploy other Lambda functions similarly

3. Deploy Frontend

cd frontend
aws s3 sync . s3://your-frontend-bucket/

4. Configure API Gateway

• See Implementation Guide

• Architecture Diagram - System design and data flow
• Implementation Log - Step-by-step implementation
• Presentation Document - Complete project overview
• Bug Fix Documentation - PNG transparency fix
• Testing Results - Performance analysis
• Test Data - Detailed test results

• IAM roles with least-privilege access
• Pre-signed URLs with time expiration
• No AWS credentials in frontend
• CORS configuration
• S3 bucket encryption (AES-256)

Current Usage: $0.00 (within AWS Free Tier)

AWS Free Tier Limits:

• Lambda: 1M requests + 400,000 GB-seconds/month
• S3: 5 GB storage + 20,000 GET + 2,000 PUT requests
• API Gateway: 1M API calls/month

Projected Cost (10,000 images/month): ~$3.15/month

• Anshuman - Team Lead + Frontend Developer

  • API Gateway setup
  • Frontend development
  • Integration and testing
  • Bug fix (PNG transparency)

• Shivam - Backend Developer

  • Image processing Lambda
  • Resize and compression logic

• Siddhant - Infrastructure Specialist

  • S3 buckets configuration
  • IAM roles and permissions
  • Pre-signed URL Lambda functions

• None currently

• WebP format conversion
• Watermarking support
• Custom resolution selection
• Batch processing
• AWS Cognito authentication
• Real-time progress updates

This project is licensed under the MIT License.

• AWS for providing excellent cloud services
• Pillow library for image processing capabilities
• The open-source community

For questions or feedback, please open an issue on GitHub.

Built with ❤️ using AWS Serverless Architecture