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Adaptive approximate nearest neighbor search for fractal image compression
1Dept. of Math., Hong Kong Baptist Univ., Kowloon.
Summary
This study enhances fractal image compression by optimizing the search for matching image blocks. The new method significantly reduces encoding time and memory usage while improving image quality and compression ratios.
Area of Science:
- Computer Science
- Image Processing
- Data Compression
Background:
- Fractal image encoding is computationally intensive due to exhaustive block matching.
- Existing methods struggle with efficiency and resource demands.
Purpose of the Study:
- To accelerate fractal image encoding by reformulating block matching as a nearest neighbor search problem.
- To improve compression ratio, fidelity, memory efficiency, and encoding time.
Main Methods:
- Developed an improved approximate nearest neighbor search formulation.
- Utilized orthogonal projection and pre-quantization of fractal transform parameters.
- Derived an optimal adaptive scheme for approximate search parameters.
Main Results:
- Achieved significant reductions in memory requirements and encoding time.
- Demonstrated improvements in both image fidelity and compression ratio.
- Validated the effectiveness of the proposed approximate nearest neighbor search technique.
Conclusions:
- The novel approach offers a substantial performance enhancement for fractal image encoding.
- This method provides a more efficient and effective solution for image compression.
- The optimized search strategy addresses key limitations of traditional fractal encoding.
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