PVQ Codebook Mapping for Low-Storage Audio Rate Adaptation
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Solution Overview
Problem
Pyramid vector quantization (PVQ) requires significant storage for V-values, which grows rapidly with higher dimensions, making explicit storage impractical for large-scale implementations.
Innovation Solution
Generating a compact map using structural unification and relational compression to reduce storage requirements, allowing for the generation of V-values as needed instead of storing them explicitly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all V-values are explicitly stored for PVQ, then complete codebook coverage is achieved, but storage requirements grow rapidly with higher dimensions
Solution Approach 1:
The codebook is segmented into multiple codebook subspaces, each storing only a portion of the total V-values. The encoder divides the high-dimensional codebook into manageable segments that can be stored efficiently, while still providing complete coverage through the combination of segments across different codebook indices.
Solution Approach 2:
The patent introduces a new dimension of codebook selection by using codebook index parameters to select among multiple codebook subspaces. Instead of storing all V-values in a single large table, the system uses additional indexing dimensions to navigate and reconstruct the full codebook content on-demand during encoding and decoding.
2Quantity of substance
If a compact map is used to generate V-values, then storage requirements are reduced, but computational complexity increases
Solution Approach 1:
The compact map is pre-computed and stored during a setup phase, capturing the relationships between codebook indices and V-values. This preliminary action allows the runtime encoding/decoding process to efficiently generate V-values through simple map lookups and combinations, rather than performing complex computations during audio processing.
Solution Approach 2:
The system creates a compressed representation (compact map) that copies only the essential structural information of the full codebook. This compact copy can be stored efficiently and used to reconstruct any needed V-values through deterministic generation rules, avoiding the need to store or transmit the complete codebook.
3Adaptability or versatility
If codebook subspaces are used with different numbers of coefficients, then flexibility is improved, but mapping complexity increases
Solution Approach 1:
Different codebook subspaces are designed with locally optimized properties, where each subspace is tailored to specific ranges of coefficients or pulse numbers. This local quality approach allows each subspace to be highly efficient for its intended purpose while the overall system maintains flexibility through the combination of specialized subspaces.
Data Source
AI summary
An example apparatus includes a memory configured to store the audio data; and one or more processors in communication with the memory, the one or more processors configured to: decode, from an encoded audio bitstream, a unique identifier for each of a plurality of subbands of audio data; perform inverse pyramid vector quantization (PVQ) using a compact map to reconstruct a residual vector for each subband of the plurality of subbands of the audio data based on the unique identifier for the respective subband of the plurality of subbands of the audio data, wherein the compact map is generated using structural unification of vectors across subbands and relational compression, and wherein the unique identifiers correspond to codevectors; and reconstruct, based on the residual vectors and energy scalars for each subband, the plurality of subbands of the audio data.