數據壓縮算法：JBIG-KIT

JBIG-KIT 實現了一個高效的數據壓縮算法，可用于雙層高分辨率的圖像壓縮，例如傳真頁和掃描文檔。它提供了可移植的 C 庫實現壓縮和解壓功能，同時提供了命令行的處理工具。

JBIG1 壓縮算法提供以下特性：

• Close to state-of-the-art lossless compression ratio for high resolution bi-level images.
• About 1.1 to 1.5 times better compression ratio on typical scanned documents compared to G4 fax compression (ITU-T T.6), which has been the best compression algorithm for scanned documents available prior to JBIG1.
• Up to 30 times better compression of scanned images with dithered images compared to G4 fax compression.
• About 2 times better compression on typical 300 dpi documents compared to “gzip -9” on raw bitmaps.
• About 3–4 times better compression than GIF on typical 300 dpi documents.
• Even much better competitive compression results on computer generated images, which are free of scanning distortions.
• JBIG1 supports hierarchical “progressive” encoding, meaning that it is possible to encode a low resolution image first, followed by resolution enhancement data, without adding a lot of redundancy. This allows, for instance, a document browser to display already a good 75 dpi low resolution version of an image, while the data necessary to reconstruct the full 300 dpi version for laser printer reproduction is still arriving (say over a slow network link or mass storage medium).
• The various resolution layers of a JBIG1 image in progressive encoding mode combined require not much more space than a normal non-progressive mode encoded image (which JBIG1 also supports).
• The progressive mode utilizes a sophisticated resolution-reduction algorithm to generate high-quality low resolution versions that preserve the shape of characters as well as the integrity of thin lines and dithered images.
• JBIG1 supports multiple bit planes. Therefore, it can also be used for grayscale and color images. While its main field of application are bi-level images, that is images with only two different pixel values (black and white), JBIG1 has also been reported to perform better than JPEG’s original lossless mode on grayscale images that have up to 6 bits per pixel.

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