- Developed a Streamlit app for efficient compression of satellite images using Discrete Cosine Transform (DCT) and Huffman Coding
- Applied DCT to transform images into frequency domain for better energy compaction
- Used Huffman coding for lossless compression of quantized DCT coefficients
- Achieved high compression ratios while preserving essential image details for satellite data analysis
- Extended the approach towards video compression in experimental phases
- Gained practical knowledge in image and video processing fundamentals
This mini-project focuses on compressing satellite images, which are often large and data-heavy, using classic but powerful methods: DCT and Huffman Coding. DCT helps convert image pixels into a format that concentrates energy in fewer coefficients, while Huffman coding compresses these coefficients efficiently by assigning shorter codes to frequent values.
I built this as a Streamlit application to make compression interactive and visually understandable, helping me explore and demonstrate these key image processing techniques. This project broadened my understanding of how satellite imagery can be optimized for storage and transmission, critical in many real-world applications.
| 🔧 Technology | ⚙️ Purpose & Role |
|---|---|
| Python + Streamlit | Frontend and backend integration with user-friendly UI for compression visualization |
| NumPy | Mathematical operations and DCT implementation |
| OpenCV | Image handling and preprocessing |
| Huffman Coding Algorithm | Lossless compression of quantized DCT coefficients |
| Matplotlib | Visualization of compression results and image comparisons |
- Explored the transformation of spatial image data to frequency domain using DCT
- Implemented Huffman encoding for efficient bit-level compression
- Balanced compression ratio with visual quality preservation for satellite imagery
- Created an interactive web app to demonstrate real-time compression effects
- Developed foundational skills useful for future projects in image and video processing
I look forward to integrating more advanced image and video processing techniques in upcoming projects, such as wavelet transforms, deep learning-based compression, and real-time video streaming optimization. The knowledge gained here provides a strong base for working with large-scale satellite data and multimedia applications.
🧢 Sumdiboii – Student & Aspiring Image Processing Enthusiast
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