Granular Partitioning and Adaptive Encoding: A Synergistic Approach to Lossless Image Compression
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Abstract
Due to the ever-increasing need for efficient digital image storage and transmission, lossless compression techniques that preserve image integrity are crucial. However, achieving high compression ratios while maintaining quality remains a challenge. This paper addresses this gap by proposing Granular Partitioning and Adaptive Encoding, a novel approach for enhanced lossless image compression. Traditional methods often employ fixed-size image blocks, neglecting the inherent variability within images. Our approach tackles this by introducing content-adaptive partitioning, which intelligently segments images into smaller regions based on features like texture and smoothness. This allows for more targeted compression strategies. Furthermore, adaptive encoding applies different compression algorithms to distinct partitions based on their specific content. This ensures optimal compression for each image region. We evaluate the proposed framework using a comprehensive image dataset and compare its performance against existing state-of-the-art methods. This research demonstrates that the synergistic combination of granular partitioning and adaptive encoding significantly improves lossless compression ratios while maintaining image quality. This approach is applicable to various image processing tasks where efficient storage and transmission are essential. Performance is measured using metrics like compression ratio and computational complexity.
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