Adaptive Audio Encoding with Multi-Lookup Table Quantization
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Solution Overview
Problem
Conventional digital signal encoding methods using psycho-acoustic models are complex and time-consuming, and those using single lookup tables are limited in adaptively encoding signals with varying characteristics, while nonlinear quantizers are complex and not suitable for low-complexity audio encoders, especially at variable bitrates.
Innovation Solution
A method and apparatus that use multiple lookup tables based on signal characteristics for adaptive encoding, reducing the complexity of nonlinear quantizers and including frame length information in the bitstream for efficient encoding and decoding at variable bitrates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If psycho-acoustic model is used for digital signal encoding, then sound quality is improved, but calculation complexity increases and encoding time is extended
Solution Approach 1:
The patent replaces the complex psycho-acoustic model with a simple lookup table that stores pre-calculated bit allocation values. This lookup table acts as a 'cheap' alternative to the computationally expensive psycho-acoustic model, providing sufficient encoding quality without the heavy calculation burden. The lookup table can be quickly referenced during encoding rather than performing real-time psycho-acoustic analysis.
Solution Approach 2:
The patent pre-calculates and stores bit allocation values in a lookup table before the actual encoding process. This preliminary action eliminates the need for complex real-time calculations during encoding, as the system simply retrieves pre-computed values from the lookup table based on signal characteristics.
2Device complexity
If single lookup table is used for encoding, then calculation complexity is reduced, but adaptability to different signal characteristics deteriorates
Solution Approach 1:
The patent divides a single lookup table into multiple lookup tables, each tailored to specific signal characteristics (e.g., different table types for different signal types or conditions). This segmentation allows the system to select the most appropriate lookup table based on the input signal characteristics, thereby improving adaptability while keeping each individual lookup table simple.
Solution Approach 2:
The patent introduces dynamic selection of lookup tables based on signal characteristics. Instead of using a fixed single lookup table, the system dynamically chooses which lookup table to use by analyzing signal properties, making the encoding process adaptable to different signal types while maintaining simplicity.
3Manufacturing precision
If nonlinear quantizer is used, then quantization performance is improved, but device complexity increases making it unsuitable for low-complexity encoders
Solution Approach 1:
The patent replaces the complex nonlinear quantizer with a simpler linear quantizer that uses lookup tables for bit allocation. This substitution maintains acceptable quantization performance while dramatically reducing device complexity, making the encoder suitable for low-complexity applications such as mobile devices or real-time processing systems.
4Productivity
If variable bitrate encoding is used, then encoding efficiency is improved, but frame length information management becomes complex
Solution Approach 1:
The patent stores frame length information at the end of each frame during the encoding process. This preliminary organization of frame length information simplifies subsequent decoding and random access operations, as the frame structure and length data are already prepared and easily locatable without complex management during variable bitrate encoding.
Data Source
AI summary
A method and an apparatus for encoding/decoding a digital signal are provided. First, a digital input signal is transformed into samples to remove redundant information among signals. Then, a lookup table corresponding to a characteristic of the input signal is selected among a plurality of lookup tables that indicate different numbers of bits allocated for each quantization unit depending on different characteristics of input signals, and the number of bits allocated for each quantization unit is acquired from the selected lookup table. Next, a distribution of samples within each quantization unit is divided into a predetermined number of sections, and the samples are linearly quantized using the allocated number of bits on a section-by-section basis. Thereafter, a bitstream comprised of frames is produced from the quantized samples and predetermined side information so that information about a frame length is stored in the end of frame.