Audio Coefficient Encoding With Adaptive Lattice Dimensions
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Solution Overview
Problem
Existing multi-rate vector quantization encoding methods are inefficient due to fixed 8-dimensional lattice selection, leading to mismatch issues and poor encoding quality, especially when representing music signals, as they rely on statistic-based base codebooks that often fail to capture the statistical distribution of audio coefficients.
Innovation Solution
The method involves selecting dimension vectors to partition coefficients into lattice vectors, which are then mapped to lattice index vectors and subjected to lossless encoding, allowing for dynamic dimension selection based on the number of coefficients in each subband, thereby improving encoding efficiency and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed 8-dimensional lattice selection is used in multi-rate vector quantization, then the encoding structure is simple, but dimension mismatch occurs when coefficients are divided into subbands with dimensions not equal to 8
Solution Approach 1:
The patent applies dynamics by making the lattice dimension variable rather than fixed. The encoding method dynamically selects lattice dimensions (4, 8, or 16) based on the actual number of coefficients in each subband, allowing the system to adapt to different dimension requirements while maintaining encoding efficiency.
Solution Approach 2:
The patent changes the parameter of lattice dimension from a fixed value (8) to a variable parameter that can take values of 4, 8, or 16. This parameter change enables the encoding method to handle subbands with different coefficient counts, resolving the dimension mismatch problem while preserving structural simplicity through a systematic selection approach.
2Productivity
If statistic-based base codebooks are used for quantization, then encoding efficiency is improved when coefficients match the codebook distribution, but bit consumption increases significantly when coefficients do not match the codebook distribution
Solution Approach 1:
The patent applies dynamics by dynamically selecting the appropriate lattice dimension (4, 8, or 16) based on the actual coefficient count in each subband. This dynamic adaptation ensures that the quantization process matches the statistical distribution of the actual data, improving encoding efficiency while avoiding excessive bit consumption that would occur with fixed-dimension codebooks.
Solution Approach 2:
The patent changes the lattice dimension parameter to match the actual data characteristics. By selecting lattice dimensions that correspond to the number of coefficients in each subband, the system optimizes the match between the quantization structure and the statistical distribution of the input data, thereby improving encoding efficiency and controlling bit consumption.
3Adaptability or versatility
If lattice dimension is increased to match subband coefficients, then dimension compatibility is improved, but the complexity of the encoding method increases
Solution Approach 1:
The patent applies segmentation by dividing the encoding process into distinct cases based on lattice dimension (4, 8, or 16). Each case has its own optimized encoding strategy, allowing the system to handle different dimension requirements without creating a single overly complex unified approach. This segmented approach manages complexity by treating different dimension scenarios separately.
Solution Approach 2:
The patent achieves universality by creating an encoding method that can handle multiple lattice dimensions (4, 8, and 16) within a single unified framework. The method selectively applies appropriate encoding techniques based on the required dimension, making the system multi-functional while avoiding the need for separate encoding methods for each dimension, thus controlling overall complexity.
Data Source
AI summary
An encoding method, decoding method, encoder, and decoder are provided in embodiments of this invention. The encoding method comprises: selecting at least one dimension vector from at least two dimension vectors to partition the coefficients to be encoded into vectors, according to the number of the coefficients to be encoded contained in a current subband; quantizing the vectors partitioned from the coefficients to be encoded into lattice vectors according to the selected dimension, and then mapping the lattice vectors to lattice index vectors; performing lossless encoding on the lattice index vectors.