Spectral Coefficient Encoding Across Variable Audio Bit Rates
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Solution Overview
Problem
Existing audio signal encoding and decoding methods fail to adapt effectively to various bit rates and sub-band sizes in the frequency domain, limiting their efficiency and flexibility.
Innovation Solution
A method and apparatus that quantize spectral data using a first and second quantization scheme, generating a bitstream by excluding certain bits, allowing for adaptive encoding and decoding of spectral coefficients across different bit rates and sub-band sizes, incorporating a bit rate control module for multi-rate support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single quantization scheme is used for spectral coefficients, then the encoding process is simple, but the encoding efficiency and adaptability to various bit rates are limited
Solution Approach 1:
The spectral coefficients are divided into different groups that are processed using different quantization schemes. Specifically, some coefficients are quantized using uniform scalar quantization while others use trellis coded quantization, allowing the system to adapt to various bit rates without processing all coefficients uniformly
Solution Approach 2:
The quantization scheme is made dynamic by selectively applying different quantization methods (USQ or TCQ) to different spectral coefficients based on the bit rate and signal characteristics. This dynamic selection enables the encoder to optimize performance for various bit rates while maintaining manageable complexity through structured decision-making
2Measurement precision
If all spectral coefficients are encoded with high precision, then the audio quality is improved, but the bit rate increases
Solution Approach 1:
Different quantization precision levels are applied to different spectral coefficients based on their importance and characteristics. Critical coefficients receive higher precision encoding while less critical ones use lower precision, optimizing the balance between audio quality and bit rate through localized quality adjustment
Solution Approach 2:
The quantization parameters are dynamically changed based on the bit rate and signal characteristics. The encoder switches between USQ and TCQ schemes and adjusts quantization step sizes to optimize the trade-off between spectral coefficient precision and bit rate, allowing high precision where needed while controlling overall bit rate
3Productivity
If multiple quantization schemes are implemented, then the encoding efficiency across various bit rates is improved, but the device complexity increases
Solution Approach 1:
The multiple quantization schemes are segmented and applied to specific groups of spectral coefficients rather than being uniformly applied. This segmentation allows the system to leverage the strengths of both USQ and TCQ for different coefficient types, improving overall encoding efficiency while managing complexity through structured application
Solution Approach 2:
The system dynamically selects between multiple quantization schemes based on bit rate and signal characteristics. This dynamic selection mechanism enables the encoder to achieve high encoding efficiency across various bit rates by choosing the most appropriate scheme for each situation, while the structured decision-making process keeps device complexity manageable
Data Source
AI summary
A spectrum coding method includes quantizing spectral data of a current band based on a first quantization scheme, generating a lower bit of the current band using the spectral data and the quantized spectral data, quantizing a sequence of lower bits including the lower bit of the current band based on a second quantization scheme, and generating a bitstream based on a upper bit excluding N bits, where N is 1 or greater, from the quantized spectral data and the quantized sequence of lower bits.