HOA Vector Reconstruction via Code Vector Segmentation
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Solution Overview
Problem
Current technologies face challenges in efficiently representing and coding higher-order ambisonic (HOA) audio signals, particularly in terms of bit-rate optimization and adaptability to different speaker geometries and acoustic conditions during playback.
Innovation Solution
The techniques involve decomposing v-vectors of HOA audio signals into a weighted sum of code vectors, selecting a subset of weights and corresponding code vectors, quantizing the selected weights, and indexing the code vectors to improve bit-rates and flexibility in audio encoding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If scalar quantizing and dequantizing the HOA representation is used, then the coding process is simple, but the bit-rate efficiency is insufficient
Solution Approach 1:
The v-vector is segmented into multiple code vectors through decomposition, where each code vector represents a specific spatial direction. This segmentation allows selective coding of only the most significant code vectors based on their energy or importance, thereby improving bit-rate efficiency while maintaining coding structure
Solution Approach 2:
The patent extracts and codes only the significant portion of the HOA signal representation. By identifying and coding only the most important code vectors that contribute most to the spatial audio quality, the system achieves better bit-rate efficiency without coding the entire HOA representation
2Measurement precision
If full HOA coefficients are coded, then the spatial audio quality is maintained, but the bit-rate increases
Solution Approach 1:
Different code vectors are treated differently based on their local importance to spatial audio quality. The patent identifies and prioritizes coding of code vectors that contribute most to spatial perception in critical directions, while using fewer bits for less significant directions, achieving efficient bit-rate allocation
Solution Approach 2:
The patent changes the representation parameters by decomposing the v-vector into code vectors and using selective quantization strategies. By transforming the HOA coefficients into a code vector representation and adjusting the number of significant code vectors coded, the system optimizes the balance between spatial audio quality and bit-rate
3Reliability
If the HOA representation is rendered to multi-channel formats, then backward compatibility is achieved, but the adaptability to different speaker geometries is limited
Solution Approach 1:
The patent creates a universal coding framework that can serve multiple functions: it maintains backward compatibility with traditional multi-channel formats while simultaneously supporting adaptive rendering to various speaker geometries. The code vector representation can be decoded to either traditional channel formats or rendered to arbitrary speaker configurations
Data Source
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AI summary
In general, techniques are described for coding of vectors decomposed from higher order ambisonic coefficients. A device comprising a processor and a memory may perform the techniques. The processor may be configured to obtain from a bitstream data indicative of a plurality of weight values that represent a vector that is included in a decomposed version of the plurality of HOA coefficients. Each of the weight values may correspond to a respective one of a plurality of weights in a weighted sum of code vectors that represents the vector and that includes a set of code vectors. The processor may further be configured to reconstruct the vector based on the weight values and the code vectors. The memory may be configured to store the reconstructed vector.