Tuple-Based Entropy Coding With Compact Huffman Codebooks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing entropy coding techniques for multi-channel audio signals require large Huffman codebooks, which increase storage space and transmission overhead, especially in limited resource applications like mobile audio streaming, due to the need to transmit and store the codebook with encoded data.
Innovation Solution
The proposed solution involves grouping information values into tuples and using an encoding rule that assigns the same code word to tuples with identical values in different orders, along with order information, to reduce the size of the Huffman codebook by exploiting symmetries in the probability distribution, such as differential encoding and symmetry operations to reduce the number of possible tuples and codebook size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional entropy coding with large Huffman codebooks is used, then compression performance is optimized, but storage space and transmission overhead increase significantly
Solution Approach 1:
The patent segments the Huffman codebook into multiple smaller codebooks organized in a tree structure. Instead of using one large codebook, the encoding process divides data into partitions and applies appropriate smaller codebooks from different levels of the tree, thereby reducing the size of any single codebook while maintaining overall compression efficiency.
Solution Approach 2:
The patent introduces a hierarchical tree structure with multiple levels, adding a dimensional aspect to codebook organization. Codebooks are arranged in a tree where each level contains multiple codebooks, and the selection of which codebook to use depends on the data characteristics and partition being encoded, enabling efficient navigation through the codebook structure.
2Reliability
If large Huffman codebooks are transmitted with encoded data, then decoding accuracy is maintained, but transmission overhead increases
Solution Approach 1:
The patent segments the codebook transmission into multiple smaller codebooks distributed across different partitions and levels. Each partition receives only the relevant smaller codebooks needed for decoding, rather than transmitting the entire large codebook, thereby reducing transmission overhead while ensuring accurate decoding through appropriate codebook selection.
Solution Approach 2:
Different partitions of the encoded data are assigned different codebooks from the tree structure based on their specific characteristics. Each partition receives a tailored set of codebooks optimized for its local data properties, ensuring high decoding accuracy for each partition while minimizing the total transmission overhead through localized codebook selection.
3Quantity of substance
If the codebook is reduced in size, then storage and transmission requirements are minimized, but compression performance may deteriorate
Solution Approach 1:
The patent implements a dynamic codebook selection mechanism where the appropriate codebook is chosen based on the characteristics of the data partition being encoded. The tree structure allows flexible navigation to select codebooks that best match the local data statistics, ensuring optimal compression performance is maintained even though individual codebooks are smaller in size.
Solution Approach 2:
The patent changes the parameter of codebook size by organizing codebooks in a hierarchical tree with multiple levels. Each level contains codebooks of different sizes and granularities, allowing the system to adaptively select the appropriate codebook size and detail level for each partition, thereby maintaining compression performance while reducing overall storage requirements through parameter adaptation.
Data Source
AI summary
The present invention is based on the finding that an efficient code for encoding information values can be derived, when two or more information values are grouped in a tuple in a tuple order and when an encoding rule is used, that assigns the same code word to tuples having identical information values in different orders and that does derive an order information, indicating the tuple order, and when the code word is output in association with the order information.