Recursive Huffman File Compression for Multi-Level Encoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data encoding technologies, such as QR codes and single-level Huffman encoding, limit data storage and transfer efficiency and can cause data corruption, necessitating a more reliable and efficient compression method.
Innovation Solution
The proposed system employs a recursive Huffman encoding technique using a master tree to create a compressed code, which is then represented and stored using bi-dimensional images, allowing for higher compression rates and efficient data storage and transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If single-level Huffman encoding is used, then data compression is achieved, but compression rate is limited and data storage efficiency is reduced
Solution Approach 1:
The patent divides the encoding process into multiple levels. The first level applies Huffman encoding to the original data to generate initial compressed code. The second level applies Huffman encoding again to the results of the first level. This multi-level segmentation of the compression process enables significantly higher compression rates compared to single-level encoding, directly resolving the contradiction between achieving compression and maximizing compression rate.
Solution Approach 2:
The patent implements nested Huffman encoding where the output of the first Huffman encoding process becomes the input for the second Huffman encoding process. This nesting of compression operations within compression operations allows for progressive compression, achieving much higher overall compression rates while maintaining data integrity, thus resolving the limitation of single-level Huffman encoding.
2Loss of substance
If more aggressive file compression techniques are applied, then data size is reduced, but data corruption or loss occurs
Solution Approach 1:
The patent creates multiple copies of the compressed data at different compression levels. The original data is preserved, and compressed versions are generated through multiple Huffman encoding passes. This copying approach allows for aggressive compression to be applied while maintaining the ability to recover the original data, thus resolving the contradiction between reducing data size and maintaining data integrity.
Solution Approach 2:
The patent performs preliminary Huffman encoding to create compressed representations before final storage or transmission. The multi-level encoding process is completed in advance, with all necessary encoding steps performed before the compressed data is finalized. This preliminary action ensures that the compression is optimized while the systematic approach maintains data integrity, resolving the contradiction between aggressive compression and data reliability.
3Quantity of substance
If QR code based images are used for data storage, then data can be stored visually, but storage capacity is limited to 4296 characters
Solution Approach 1:
The patent transitions from traditional linear data storage to multi-dimensional compressed representation. By applying Huffman encoding multiple levels and representing the compressed data in various formats including visual representations, the system effectively adds dimensional complexity to the storage approach. This enables storage of much more than 4296 characters while maintaining visual representation capabilities, resolving the contradiction between storage capacity and storage flexibility.
Data Source
AI summary
Examples of the disclosure describe systems and methods for implementing a file compression system. In an example method, a source string to be compressed is received. The source string comprises a plurality of characters. A first frequency is determined for each character of the plurality of characters of the source string. A first tree corresponding to the source string is determined based on the first frequencies. The source string is encoded using the first tree to generate a first encoded string. It is determined whether a total number of bits in the first encoded string is a multiple of eight. In accordance with a determination that the total number of bits in the first encoded string is not a multiple of eight, the first encoded string is appended with zeroes so that a new total number of bits in the first encoded string is a multiple of eight. In accordance with a determination that the total number of bits in the first encoded string is a multiple of eight, the method forgoes appending the first encoded string with zeroes. The first encoded string is divided into one or more eight-bit segments and a placeholder character is assigned to each eight-bit segment. A second frequency for each of the placeholder characters in the first encoded string is determined. A second tree corresponding to the first encoded string is determined based on the second frequencies. The first encoded string is encoded using the second tree to generate a second encoded string.