Spatial Component Quantization for Scene-Based Audio Bitrate Efficiency
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Solution Overview
Problem
Current psychoacoustic audio coding techniques face challenges in efficiently encoding and decoding scene-based audio data, particularly in accurately representing spatial components, which affects the quality of the reconstructed soundfield due to quantization errors and dynamic range issues.
Innovation Solution
A device and method that perform spatial audio encoding and psychoacoustic audio encoding to obtain foreground and spatial components, scale and quantize the spatial components based on bit allocation, and specify them in a bitstream, incorporating a spatial component quantizer to reduce quantization errors and improve spatial accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If spatial components are quantized using conventional psychoacoustic audio coding techniques, then bitrate efficiency is improved, but spatial accuracy and soundfield fidelity deteriorate due to quantization errors
Solution Approach 1:
The audio signal is segmented into foreground audio signals and spatial components, allowing independent processing. The spatial components are further segmented into multiple bands that can be quantized with different precision levels, enabling optimized bitrate allocation that preserves spatial accuracy while maintaining efficiency.
Solution Approach 2:
Different quantization precision is applied to different spatial component bands based on their perceptual importance. Critical spatial frequencies receive higher precision quantization to maintain spatial accuracy, while less critical bands use coarser quantization to improve bitrate efficiency.
2Measurement precision
If spatial components are encoded with high precision to maintain spatial accuracy, then soundfield fidelity is improved, but bitrate consumption increases
Solution Approach 1:
The quantization step size for spatial components is dynamically adjusted based on the allocated bitrate and perceptual importance of different spatial frequency bands. This parameter change allows the system to maintain spatial accuracy where critical while reducing bitrate consumption in less critical regions.
Solution Approach 2:
Instead of applying uniform high precision quantization to all spatial components, the system applies high precision only to the most perceptually critical spatial bands, accepting partial precision loss in less important bands to achieve overall bitrate efficiency.
3Device complexity
If uniform quantization is applied to spatial components, then encoding complexity is reduced, but spatial resolution and soundfield reconstruction quality deteriorate
Solution Approach 1:
The quantization process transitions from static uniform quantization to dynamic non-uniform quantization, where quantization parameters are adapted based on the allocated bitrate and perceptual characteristics of different spatial frequency bands, improving spatial resolution without excessive complexity increase.
Data Source
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AI summary
In general, techniques are described by which to code scaled spatial components. A device comprising a memory and one or more processors may be configured to perform the techniques. The memory may store a bitstream including an encoded foreground audio signal and a corresponding quantized spatial component. The one or more processors may perform psychoacoustic audio decoding with respect to the encoded foreground audio signal to obtain a foreground audio signal, and determine, when performing psychoacoustic audio decoding, a bit allocation for the encoded foreground audio signal. The one or more processors may dequantize the quantized spatial component to obtain a scaled spatial component, and descale, based on the bit allocation, the scaled spatial component to obtain a spatial component. The one or more processors may reconstruct, based on the foreground audio signal and the spatial component, scene-based audio data.