HOA Spatial Coding With Perceptual Channel Ranking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing SPAR coding framework for Ambisonics is limited to First Order Ambisonics and needs expansion to Higher Order Ambisonics to enhance performance within the IVAS framework, particularly for bitrate distribution and audio quality.
Innovation Solution
A method of encoding and decoding Higher Order Ambisonics (HOA) audio using a Spatial Reconstruction (SPAR) coding framework, where the selection of prediction residuals is based on a threshold number for directly coded channels, determined by bitrate limitations, metadata size, core codec performance, and audio quality, with channel ranking prioritizing perceptual importance and overlap with spatial planes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the SPAR coding framework is expanded to Higher Order Ambisonics, then audio quality and performance are enhanced, but device complexity and computational requirements increase
Solution Approach 1:
The HOA encoding process is segmented into multiple stages: prediction residual generation, channel ranking based on perceptual importance, selective direct coding of top-ranked channels, and parametric coding of remaining channels. This segmentation allows the system to manage complexity by breaking down the high-order encoding into manageable steps while maintaining audio quality.
Solution Approach 2:
Different coding strategies are applied to different HOA channels based on their perceptual importance. High-ranked channels receive direct coding with higher quality, while lower-ranked channels use parametric coding. This local quality approach optimizes the balance between overall audio quality and computational complexity by allocating resources selectively.
2Reliability
If more Ambisonics channels are directly coded, then audio quality improves, but bitrate consumption increases
Solution Approach 1:
The system dynamically changes coding parameters (number of directly coded channels vs. parametrically coded channels) based on bitrate constraints and perceptual importance rankings. This allows flexible adaptation of bitrate consumption while maintaining optimal audio quality for each channel according to its perceived importance to the overall spatial audio experience.
3Reliability
If channel ranking is based on perceptual importance, then audio quality is optimized, but computational complexity increases due to additional processing
Solution Approach 1:
Channel ranking based on perceptual importance is performed as a preliminary step before the actual encoding process. By pre-establishing the priority order of HOA channels, the system avoids repeated complexity-intensive calculations during encoding, as the ranking determines the encoding strategy for all subsequent processing stages.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Described herein is a method of encoding Higher Order Ambisonics, HOA, audio, the method including: receiving an input HOA audio signal having more than four Ambisonics channels; encoding the HOA audio signal using a SPAR coding framework and a core audio encoder; and providing the encoded HOA audio signal to a downstream device, the encoded HOA audio signal including core encoded SPAR downmix channels and encoded SPAR metadata. Further described are a method of decoding Higher Order Ambisonics, HOA, audio, respective apparatuses and computer program products.