Hierarchical Dictionary Compression for Multi-Page Binary Documents
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Solution Overview
Problem
Existing binary document image compression methods, such as JBIG2, do not effectively utilize information redundancy across multiple pages, leading to suboptimal compression ratios due to memory constraints and inefficiencies in dictionary management.
Innovation Solution
A dynamic hierarchical dictionary design that builds and updates dictionaries dynamically, utilizing conditional entropy estimation to retain and reuse information from previous pages, while managing memory constraints by discarding least distinct entries, thereby improving compression ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional JBIG2 compression methods are used with fixed dictionary management, then implementation is simple, but compression ratio is suboptimal due to inability to utilize information redundancy across multiple pages
Solution Approach 1:
The patent implements dynamic dictionary management where the dictionary is updated across multiple pages based on information redundancy analysis. The dictionary evolves by adding new symbols from subsequent pages and removing least distinct symbols when memory constraints are reached, transforming the static dictionary approach into a dynamic adaptive system that improves compression ratio
Solution Approach 2:
The patent segments the dictionary management process into distinct operations: symbol extraction from current page, dictionary update by adding new symbols, redundancy analysis to identify least distinct symbols, and selective removal of those symbols. This segmentation allows complex dictionary management to be handled through modular, manageable steps
2Productivity
If more dictionary entries are retained to improve compression ratio, then information redundancy is better utilized, but memory constraints are violated
Solution Approach 1:
The patent changes the parameter of dictionary size dynamically based on memory availability and information redundancy analysis. Instead of maintaining a fixed dictionary size, the system adjusts the number of dictionary entries by adding symbols that provide high compression benefit and removing symbols with low distinctiveness, optimizing the balance between compression ratio and memory usage
Solution Approach 2:
The patent implements a discard-and-recover mechanism where least distinct symbols are discarded from the dictionary when memory constraints are reached, and potentially recovered in future pages if they prove to be valuable for compression. This allows the system to manage memory constraints while maintaining the ability to improve compression ratio when beneficial symbols are encountered
3Productivity
If dictionary size is increased to capture more information redundancy, then compression effectiveness improves, but file size penalty increases
Solution Approach 1:
The patent applies partial action by selectively adding only those symbols to the dictionary that provide significant compression benefit, rather than adding all possible symbols. The system performs redundancy analysis to identify which symbols are worth storing, avoiding the file size penalty associated with storing unnecessary dictionary entries while maintaining compression effectiveness
Data Source
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AI summary
A method of creating a hierarchical dictionary comprises, with a processor, extracting a number of symbols from a first image, constructing a number of refinement dictionary entries based on the symbols, the refinement dictionary entries forming a refinement dictionary, grouping a number of the refinement dictionary entries into clusters to form a number of refinement dictionary entry clusters, and constructing a number of direct dictionary entries for each of the refinement dictionary entry clusters, the direct dictionary entries forming a direct dictionary.