Analysis of PDF Compression Principles and Techniques

I. Intelligent Compression of Image Resources

1. Resolution Optimization
   Image resolution directly affects file size. For example, print-quality PDFs typically use a resolution of over 300dpi, while for electronic reading scenarios, non-critical images can be reduced to 72-150dpi. Through intelligent sampling techniques (such as the bicubic interpolation algorithm), the total number of pixels can be reduced while maintaining visual acceptability.
2. Encoding Algorithm Adaptation
   • Lossy Compression: JPEG encoding is used for photographic images. By removing high-frequency details through Discrete Cosine Transform (DCT), a typical compression ratio of 10:1 can be achieved.
   • Lossless Compression: For text and line drawings, Flate (ZIP) or CCITT Fax Group 4 encoding is used. The former is suitable for grayscale images, while the latter is designed specifically for black-and-white binary images, achieving lossless compression to 1/5 of the original size.
   • Color Space Conversion: Converting 24-bit RGB images to an 8-bit indexed color mode, combined with an optimized palette, can reduce data volume by two-thirds without significant distortion.
3. Reuse of Duplicate Resources
   By comparing hash values, duplicate image resources in the document are identified. Only a single instance is retained and referenced in multiple locations to eliminate redundant storage. This technique is particularly effective for documents containing the same logo or watermark.

II. Precise Processing of Font Resources

1. Dynamic Subset Embedding
   Traditional PDF embedding of complete fonts includes thousands of unused glyphs. Dynamic subset embedding extracts only the characters actually used in the text. For example, if a document only contains the text “Hello 2024,” the English characters and numbers can be extracted separately, reducing the font file size by over 90%.
2. Font Type Optimization
   Prioritize the use of CID fonts over outdated formats like Type1. Adopt more efficient OpenType CFF compression and clean up obsolete metadata tables in the fonts (such as the DSIG digital signature).

III. In-depth Restructuring of Content Streams

1. Vector Instruction Optimization
   • Path Simplification: Merge consecutive line-drawing commands. For example, replace 10 moveto-lineeto operations with a single polygon-drawing command.
   • Graphics State Management: Automatically detect repeated color settings and line width definitions, and reuse them through object references to reduce redundant code.
2. Incremental Encoding Technology
   Apply the FlateDeflate algorithm for secondary compression of text content streams, combining LZ77 dictionary encoding with Huffman encoding. This can compress ASCII text to 25% of its original size. Perform entropy analysis on already compressed content to avoid size expansion caused by redundant compression.

IV. Systematic Optimization of Document Structure

1. Linearization of Object Storage
   Traditional PDFs use a cross-reference table (xref) for random access. By reordering object storage to create a “linearized PDF,” the file header includes key indexes, improving progressive loading efficiency in network environments.
2. Metadata Cleanup
   • Remove obsolete XMP metadata and annotation layer information.
   • Merge content streams from multiple historical versions.
   • Eliminate hidden thumbnail preview data.
3. Flattening of Tree Structures
   Convert the page tree (Page Tree) to a flattened list, reducing intermediate node levels and reducing document structure size by 15%-30%.

V. Balanced Selection of Compression Strategies

1. Quality Control Dimensions

2. Format Compatibility Design
   • Enable Object Stream technology for PDF 1.5+ to combine multiple small objects into a single storage.
   • Retain ASCIIHex encoding fallback mechanism for compatibility with older readers.

VI. Compression Effect Comparison Experiment

• Lossless Mode: Size reduced to 14.2MB (24% reduction), retaining all text editability.
• Balanced Mode: Size reduced to 9.3MB (50% reduction), with minor image blurring but clear text.
• Extreme Compression: Size reduced to 3.8MB (80% reduction), with some charts showing mosaic effects.

Technical Limitations and Future Trends

• Encrypted documents cannot be restructured.
• Excessive simplification of vector graphics may cause printing distortion.
• Transparent layer blending may increase computational complexity.

• AI-based semantic image compression (e.g., high compression for background areas).
• Adaptive document type recognition systems.
• Preliminary exploration of quantum compression algorithms.