Non-Uniform Audio Parameter Quantization for Lower Bit Consumption
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Solution Overview
Problem
Existing audio coding systems face challenges in minimizing bit stream information while maintaining audio quality, as high compression leads to poor sound quality due to complex calculations or lost information, and low compression results in capacity issues.
Innovation Solution
The implementation of a non-uniform scalar quantization scheme for audio parameters in parametric spatial coding, where smaller step-sizes are used for ranges of high human sound perception sensitivity and larger step-sizes for less sensitive ranges, along with a scaling function to adjust quantization based on perceived sound characteristics, reducing average bit consumption without degrading sound quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If high compression is applied to minimize bit stream information, then bit consumption is reduced, but audio quality deteriorates due to complex calculations or lost information
Solution Approach 1:
The patent applies different quantization step-sizes to different parameter ranges based on human auditory sensitivity. Fine quantization (small step-sizes) is used for parameters in ranges where human hearing is most sensitive, while coarse quantization (large step-sizes) is used for less sensitive ranges. This local differentiation optimizes the balance between bit consumption and perceived audio quality.
Solution Approach 2:
The patent changes the quantization parameter (step-size) dynamically based on the parameter value and human auditory sensitivity characteristics. By adapting the quantization granularity to match perceptual sensitivity, the system achieves efficient compression while maintaining quality where it matters most to human listeners.
2Reliability
If low compression is applied to maintain audio quality, then information is preserved, but bit stream capacity problems occur
Solution Approach 1:
Instead of uniformly preserving all information, the patent selectively preserves information based on local auditory sensitivity. Critical parameters in sensitive ranges are preserved with high precision, while less critical parameters in insensitive ranges are compressed more aggressively, optimizing bit stream capacity utilization.
Solution Approach 2:
The patent applies partial preservation of information by using fine quantization only where necessary (in sensitive ranges) and coarse quantization elsewhere. This partial action approach avoids the excessive bit consumption that would result from uniform fine quantization across all parameter ranges.
3Ease of manufacture
If uniform quantization is used for all parameter ranges, then implementation is simple, but bit efficiency is suboptimal due to lack of perceptual optimization
Solution Approach 1:
The patent divides the parameter space into different regions with different quantization characteristics. By implementing local quality differentiation through multiple quantization tables or adaptive step-size selection, the system achieves superior bit efficiency while maintaining reasonable implementation complexity through structured design.
Solution Approach 2:
The patent changes the quantization parameter (step-size) based on the input parameter value and predefined sensitivity characteristics. This parameter adaptation enables perceptually optimized quantization that significantly improves bit efficiency compared to fixed uniform quantization, while maintaining implementation feasibility through lookup tables or simple decision logic.
Data Source
AI summary
The present disclosure provides methods, devices and computer program products for non-uniform quantization of parameters. The disclosure further relates to a method and apparatus for reconstructing an audio object in an audio decoding system taking the non-uniformly quantized parameters into account. According to the disclosure, such an approach renders it possible to reduce bit consumption without substantially reducing the quality of the reconstructed audio object.