Multi-Channel Audio Decoder Using Real Transform for Complexity Reduction
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Solution Overview
Problem
Existing multi-channel audio coding techniques require complex transforms, rotations, and post-processing, leading to high computational complexity and resource usage in decoders, especially when using vector quantization, which complicates the decoding process and increases memory and processing demands.
Innovation Solution
The proposed solution involves translating channel correlation matrix parameters to a real transform, replacing complex scaling and rotation with real scaling, and using a real filter, which reduces the complexity of decoding by approximately one-fourth, eliminating the need for complex filter operations and simplifying channel extension processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex transforms, rotations, and post-processing are used in multi-channel audio decoding, then audio quality is maintained, but computational complexity and resource usage increase significantly
Solution Approach 1:
The patent extracts and removes the complex transform and rotation operations from the decoding process. Instead of performing full complex transforms and rotations on all channels, the invention selectively applies simplified operations only where necessary, taking out the computationally intensive parts while maintaining essential audio quality through selective processing of coded channels.
Solution Approach 2:
The decoding process is segmented into different stages: first decoding a subset of channels fully, then using simplified operations for the remaining channels. This segmentation allows the system to apply complex operations only to necessary portions while using efficient simplified operations for others, reducing overall complexity while maintaining quality.
2Quantity of substance
If vector quantization is applied to multi-channel audio coding, then bitrate is reduced, but decoding complexity and memory requirements increase
Solution Approach 1:
The patent applies vector quantization selectively rather than uniformly across all channels. By performing full vector quantization on a subset of channels and using simplified reconstruction for others, the system achieves bitrate reduction with controlled complexity, avoiding the excessive computational burden of applying full vector quantization to all channels.
3Measurement precision
If complex filter operations are performed for channel extension, then channel reconstruction accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The patent uses computationally inexpensive operations to approximate channel reconstruction instead of expensive complex filter operations. By using simple scaling and addition operations based on decoded parameters, the system achieves acceptable reconstruction accuracy with minimal processing time, effectively using 'cheap' computational operations in place of 'expensive' ones.
Data Source
AI summary
A multi-channel audio decoder provides a reduced complexity processing to reconstruct multi-channel audio from an encoded bitstream in which the multi-channel audio is represented as a coded subset of the channels along with a complex channel correlation matrix parameterization. The decoder translates the complex channel correlation matrix parameterization to a real transform that satisfies the magnitude of the complex channel correlation matrix. The multi-channel audio is derived from the coded subset of channels via channel extension processing using a real value effect signal and real number scaling.