Energy Lossless Audio Coding Bit Allocation
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Solution Overview
Problem
Existing audio and speech signal coding methods face challenges in efficiently allocating bits to encode energy information within a limited bit range without degrading sound quality, often requiring a trade-off between encoding energy and frequency components.
Innovation Solution
The method involves selecting between a first and second coding method based on the energy quantization index range, using large or small symbol coding to optimize bit allocation for energy and frequency components, allowing for lossless coding and decoding of audio signals while reducing the number of bits used for energy encoding and increasing bits available for frequency components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of bits allocated for coding energy information is increased, then the quality of energy reconstruction is improved, but the number of bits available for coding frequency components is reduced
Solution Approach 1:
The patent segments the energy coding process into multiple stages: coarse energy quantization, fine energy quantization, and residual coding. This segmentation allows the system to allocate bits more efficiently across different energy representation levels, improving overall energy reconstruction quality while managing the total bit budget for frequency components.
Solution Approach 2:
The patent dynamically changes coding parameters based on the characteristics of the audio signal. The quantization step size, number of bits for energy coding, and bit allocation for frequency components are adjusted according to the signal's energy distribution and spectral characteristics, optimizing the trade-off between energy reconstruction quality and frequency coding precision.
2Measurement precision
If the number of bits allocated for coding frequency components is increased, then the spectral resolution is improved, but the number of bits available for coding energy information is reduced
Solution Approach 1:
The patent implements dynamic bit allocation between energy coding and frequency component coding based on the instantaneous characteristics of the audio signal. When the signal requires higher spectral resolution, more bits are allocated to frequency components; when energy reconstruction is more critical, bits are reallocated to energy coding. This dynamic adjustment optimizes the trade-off between spectral resolution and energy information precision.
Solution Approach 2:
The patent applies different coding precision to different frequency bands and time regions. Instead of uniformly allocating bits, the system identifies regions where energy information is more critical versus regions where spectral detail is more important, and allocates bits locally according to these needs, thereby improving overall coding efficiency.
3Device complexity
If a fixed bit allocation scheme is used for energy coding, then the coding complexity is reduced, but the adaptability to different signal characteristics is degraded
Solution Approach 1:
The patent performs preliminary analysis of the audio signal characteristics before the main coding process. This preliminary action includes estimating the signal's energy distribution, spectral content, and variability, which then informs the selection of appropriate quantization parameters and bit allocation strategies. This pre-processing step enables the system to adapt to different signal characteristics without requiring complex real-time adjustments during coding.
Solution Approach 2:
The coding system automatically adapts to different signal characteristics through self-adjusting mechanisms. The quantization parameters and bit allocation are determined based on the signal's own statistical properties and characteristics, eliminating the need for external control or manual configuration. This self-service approach maintains adaptability while controlling coding complexity.
Data Source
AI summary
The lossless coding method includes selecting one of a first coding method and a second coding method, based on a range in which a quantization index of energy is represented, and coding the quantization index by using the selected coding method. The lossless decoding method includes determining a coding method of a differential quantization index of energy included in a bitstream and decoding the differential quantization index by using one of a first decoding method and a second decoding method based on a range in which a quantization index of energy is represented, in response to the determined coding method.