Recursive Huffman File Compression for Higher Data Density
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Solution Overview
Problem
Current data encoding technologies, such as QR codes and single-level Huffman encoding, limit data storage and transfer efficiency, often causing data corruption or inefficiencies in compression.
Innovation Solution
The system employs a recursive Huffman technique to create a master tree for efficient encoding and decoding, using bi-dimensional images to store and represent compressed codes, thereby achieving higher average compression rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If single-level Huffman encoding is used, then encoding simplicity is maintained, but data compression efficiency is limited
Solution Approach 1:
The encoding process is divided into multiple levels: first-level Huffman encoding generates an intermediate encoded string, then second-level Huffman encoding further compresses the result. This segmentation allows each encoding stage to optimize for different aspects, achieving higher overall compression while maintaining manageable complexity through modular processing.
Solution Approach 2:
The patent implements nested Huffman encoding where the output of the first Huffman encoding process becomes the input for a second Huffman encoding process. This nested structure enables progressive compression, with each layer building upon the previous one to achieve superior compression ratios without requiring complete redesign of the encoding system.
2Productivity
If more aggressive file compression techniques are applied, then compression ratio is improved, but data integrity deteriorates
Solution Approach 1:
The patent performs preliminary padding of the encoded string with zeros before the second Huffman encoding stage. This preliminary action ensures that the data structure is properly prepared and aligned, preventing data corruption that might occur during aggressive compression. The padding is later removed during decoding, preserving data integrity while enabling more aggressive compression techniques.
Solution Approach 2:
By adding zero-padding as a cushion before the second encoding stage, the system creates a buffer that protects against potential data loss or corruption during aggressive compression. This cushioning mechanism ensures that even under aggressive compression, the original data can be fully recovered during the decoding process.
3Ease of operation
If QR code based images are used for data storage, then data transfer is enabled, but storage capacity is limited
Solution Approach 1:
The patent transitions from single-level encoded strings to two-dimensional image representations for data storage. By encoding compressed data into 2D images with multiple color levels, the system dramatically increases storage capacity while maintaining ease of transfer through visual media. The bi-dimensional image format allows much more data to be stored in the same physical space compared to traditional QR codes.
Solution Approach 2:
The patent utilizes multi-colored images where different color levels represent different data values. By using multiple colors and color intensities in the 2D image representation, the system increases the information density of the stored data, allowing significantly more data to be stored and transferred through images compared to traditional black-and-white QR codes.
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.