Multi-Channel Audio Encoding With Importance-Based Mixed Modes
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Solution Overview
Problem
Conventional encoding methods for multi-channel audio result in resource and computing power shortages due to separate storage and transmission of each channel, leading to inefficient use of resources and insufficient computing power, especially when using AI-based encoding.
Innovation Solution
Determine encoding units based on audio type, acquire importance evaluation indexes, and apply mixed-mode encoding by selecting appropriate encoding modes for each unit, balancing resource and computing power requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional encoding is used for multi-channel audio, then decoding speed is fast and sound quality is maintained at medium and high bit rates, but storage space increases and bandwidth requirements increase
Solution Approach 1:
The patent combines multiple audio channels into a single encoded stream using channel coupling technology. Instead of encoding each channel separately, the system merges spatial and temporal correlations across channels to create a compact representation that maintains sound quality while reducing storage requirements
Solution Approach 2:
The audio signal is segmented into different frequency bands and temporal frames, with importance evaluation indexes calculated for each segment. This allows selective encoding where critical segments receive higher bit rates and less critical segments use lower bit rates, optimizing the balance between quality and storage
2Reliability
If AI-based encoding is applied to multiple channels, then sound quality at low bit rates is improved, but computing power requirements exceed available resources
Solution Approach 1:
The system applies AI-based encoding selectively to important audio segments identified by importance evaluation indexes, while using conventional encoding for less critical segments. This local application of AI technology maintains sound quality where it matters most while keeping overall computing power requirements within available resources
Solution Approach 2:
Instead of applying AI encoding to all audio data, the system performs partial AI encoding only on segments that benefit most from it, determined by importance evaluation. This partial action approach achieves improved sound quality at low bit rates without the excessive computing power consumption of full AI encoding
3Adaptability or versatility
If each audio channel is stored or transmitted separately, then channel independence is maintained, but resource consumption increases and computing power becomes insufficient
Solution Approach 1:
The patent merges multiple independent audio channels into a unified encoded structure that preserves channel independence through separate decode paths while sharing common encoding resources. This allows the system to maintain the versatility of independent channel processing while reducing overall resource consumption through shared analysis and importance evaluation
Data Source
AI summary
Provided are method and apparatus for encoding a multi-channel audio, an electronic device, and a storage medium. The method includes: determining encoding units of a multi-channel audio according to an audio type of the multi-channel audio; acquiring importance evaluation indexes of the encoding units of the multi-channel audio; determining encoding modes of the encoding units respectively according to the importance evaluation indexes; and encoding the encoding units in the multi-channel audio respectively based on the encoding modes.