Adaptive Parameter Grouping for Lower-Bitrate Audio Side Information
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current multi-channel audio encoding techniques require high bit rates for side information, limiting their application in bandwidth-constrained environments such as mobile streaming, where efficient lossless compression of parameters is necessary.
Innovation Solution
The proposed solution involves a compression unit that rearranges parameters into sequences of tuples, using a bit estimator to determine the most efficient encoding sequence, either grouping spectral parameters in time or frequency, and applying a two-dimensional Huffman code to minimize bit rate, with differential encoding and adaptive grouping strategies further reducing side information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional multi-channel audio encoding techniques are used, then audio quality is maintained, but bit rate for side information becomes excessively high
Solution Approach 1:
The patent segments the side information parameters into different groups (e.g., scale factors, intensity stereo information, binaural cue parameters) and applies different encoding strategies to each group. This segmentation allows for more efficient compression by treating different parameter types according to their specific statistical characteristics and importance, thereby reducing the overall bit rate while maintaining audio quality.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting encoding precision based on signal characteristics. For example, it uses variable precision encoding where less critical parameters are encoded with lower precision and more critical parameters retain higher precision. This adaptive approach optimizes the balance between audio quality and bit rate consumption.
2Quantity of substance
If joint stereo techniques are applied to reduce data transmission, then bandwidth requirements decrease, but coding complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and encoding certain parameters in a optimized sequence. It performs preliminary grouping and sorting of parameters before the main encoding process, which simplifies the subsequent encoding steps and reduces overall coding complexity while maintaining efficient data compression.
Solution Approach 2:
The encoding system uses self-service mechanisms by automatically adapting encoding parameters based on the input signal characteristics without requiring external control. The system self-adjusts the precision and grouping of parameters based on their statistical properties, reducing the need for complex external control mechanisms while achieving efficient compression.
3Measurement precision
If high precision parameter encoding is used, then reconstruction accuracy improves, but transmission bandwidth consumption increases
Solution Approach 1:
The patent applies local quality by assigning different encoding precisions to different parameter groups based on their local importance. Critical parameters such as those affecting spatial perception are encoded with higher precision, while less critical parameters use lower precision. This localized quality adjustment maintains reconstruction accuracy for important aspects while reducing overall bandwidth consumption.
Data Source
AI summary
The present invention is based on the finding that parameters including: a first set of parameters of a representation of a first portion of an original signal and a second set of parameters of a representation of a second portion of the original signal can be efficiently encoded when the parameters are arranged in a first sequence of tuples and a second sequence of tuples. The first sequence of tuples includes tuples of parameters having two parameters from a single portion of the original signal and the second sequence of tuples includes tuples of parameters having one parameter from the first portion and one parameter from the second portion of the original signal. A bit estimator estimates the number of necessary bits to encode the first and the second sequence of tuples. Only the sequence of tuples, which results in the lower number of bits, is encoded.