Symbol Compression Coding with Chunk-Specific Table Indexing
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Solution Overview
Problem
Existing encoding methods, such as Huffman encoding, are inefficient when frequency tables are not known by both the encoder and decoder, especially for small data segments, leading to significant data overhead due to the delivery of large coding table data, which is not optimized for each data chunk.
Innovation Solution
The method involves analyzing input data, splitting it into chunks, generating optimized code tables, frequency tables, or probability tables for each chunk, and using indices to compress and encode data, allowing for efficient encoding and decoding by regenerating tables symmetrically for reuse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If Huffman encoding or VLC encoding methods are employed to compress data, then compression is achieved, but significant data overhead occurs when frequency tables are not known by both encoder and decoder
Solution Approach 1:
The input data is divided into multiple data chunks, and frequency tables are generated and delivered separately for each chunk. This segmentation allows the decoder to reconstruct the appropriate frequency table for each segment, reducing the need to deliver large universal coding tables while maintaining compression efficiency for each segment.
Solution Approach 2:
Frequency tables are generated in advance for each data chunk before encoding. The encoder delivers these pre-generated frequency tables along with the encoded data, allowing the decoder to reconstruct the exact same frequency tables and apply the same Huffman coding without needing to receive complete coding tables.
2Quantity of substance
If Arithmetic coding or Range coding is used for compression, then better compression ratios are achieved, but inefficiency occurs when frequency tables must be delivered for small data segments
Solution Approach 1:
Different frequency tables are generated and delivered for different data chunks based on their local characteristics. Each data segment receives a customized frequency table that reflects its specific symbol distribution, improving compression efficiency for small segments without requiring delivery of large universal frequency tables.
Data Source
AI summary
A method of encoding input data in an encoder to generate corresponding encoded data includes splitting and/or transforming the input data into data chunks, analyzing symbols present in the input data and compressing the symbols as a function of occurrence of the symbols in the data chunks; generating code tables, frequency tables, and/or length of code word tables for the symbols present in the data chunks; computing sets of indices relating the symbols in each data chunk and/or the compressed symbols to entries in the code tables, the frequency tables, and/or the length of code word tables; and assembling the sets of indices, together with the frequency tables, the code tables, and/or information indicative of such tables, for generating the encoded data. An encoder that utilizes the method, together with a corresponding decoder, wherein the encoder and the decoder in combination form a codec.