Lossless Image Compression Using Multi-level Dictionaries: Binary Images
Samar Agnihotri, Renu Rameshan, Ritwik Ghosal

TL;DR
This paper introduces a lossless binary image compression method using multi-level dictionaries that captures spatial patterns, outperforming existing schemes in efficiency and scalability.
Contribution
The paper proposes a novel dictionary-based compression scheme for binary images that leverages multi-scale pattern learning to improve performance and scalability.
Findings
Outperforms WebP by 1.5x on average
Surpasses state-of-the-art learning-based schemes by over 3x
Better than JBIG2 in binary image compression
Abstract
Lossless image compression is required in various applications to reduce storage or transmission costs of images, while requiring the reconstructed images to have zero information loss compared to the original. Existing lossless image compression methods either have simple design but poor compression performance, or complex design, better performance, but with no performance guarantees. In our endeavor to develop a lossless image compression method with low complexity and guaranteed performance, we argue that compressibility of a color image is essentially derived from the patterns in its spatial structure, intensity variations, and color variations. Thus, we divide the overall design of a lossless image compression scheme into three parts that exploit corresponding redundancies. We further argue that the binarized version of an image captures its fundamental spatial structure. In this…
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Taxonomy
TopicsAdvanced Data Compression Techniques · Image Retrieval and Classification Techniques
