Audio Encoder Bijective Transformation Low Bit Rate
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Solution Overview
Problem
Conventional audio signal compression techniques experience reduced compression efficiency and increased distortion at low bit rates due to integer bit number assignment per frequency spectral sample.
Innovation Solution
An encoder that combines multiple quantized spectral values into unified integer values using bijective transformations, allowing for more efficient bit assignment and encoding, while a decoder reverses this process to minimize distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If integer bit number assignment is used per frequency spectral sample, then device complexity is reduced and ease of operation is improved, but manufacturing precision (encoding precision) deteriorates and loss of information increases at low bit rates
Solution Approach 1:
The frequency spectral sequence is divided into multiple blocks, and each block is processed independently with separate bit assignment. This allows fine-grained control of bit allocation at the block level rather than being constrained to integer bits per sample, resolving the contradiction by enabling precise bit assignment while maintaining manageable processing complexity through modular block-based operation
Solution Approach 2:
The invention transitions from one-dimensional bit assignment (integer bits per individual sample) to a multi-dimensional approach where bits are allocated across multiple blocks simultaneously. This dimensional shift allows fractional bit assignment averaged over blocks, achieving higher encoding precision at low bit rates while keeping the system operable through structured block processing
2Device complexity
If integer bit number assignment is used per frequency spectral sample, then device complexity is reduced, but loss of information increases due to quantization errors at low bit rates
Solution Approach 1:
By segmenting the frequency spectral sequence into multiple blocks and performing independent bit assignment for each block, the system achieves finer-grained information preservation. The segmentation allows fractional bit allocation averaged over blocks, reducing quantization errors and information loss while maintaining relatively simple device complexity through modular processing
Solution Approach 2:
The invention changes the bit assignment parameter from integer values per sample to fractional values averaged over multiple blocks. This parameter change enables more precise representation of spectral information at low bit rates, reducing information loss while keeping device complexity manageable through the structured block-based approach
3Manufacturing precision
If multiple quantized spectral values are combined into unified integer values, then encoding precision is improved and loss of information is reduced, but device complexity increases
Solution Approach 1:
Multiple quantized spectral values within each block are merged into a unified representation that allows fractional bit assignment. This merging process improves encoding precision by enabling more efficient use of available bits across the block, while the systematic merging approach keeps device complexity manageable through regular processing patterns
Solution Approach 2:
The unified integer value representation serves multiple functions: it enables fractional bit assignment, facilitates efficient encoding, and maintains reversibility for decoding. This multi-functionality improves encoding precision while avoiding the need for separate complex processing paths, thereby limiting the increase in device complexity
Data Source
AI summary
Efficient assignment of bit numbers is performed even under a low bit rate condition. A quantizer 12 obtains a quantized spectral sequence from a frequency spectral sequence. An integer transformer 13 obtains a unified quantized spectral sequence by obtaining, by a bijective transformation, a transformed integer for each of the sets, each being made up of integer values, obtained from the quantized spectral sequence. An integer encoder 15 obtains an integer code by encoding the unified quantized spectral sequence using a bit assignment sequence. An object-to-be-encoded estimator 18 obtains an estimated unified spectral sequence from the frequency spectral sequence by a transformation which is performed by the integer transformer 13 or a transformation that approximates the magnitude relationship between values before and after the above transformation. A bit assigner 14 obtains a bit assignment sequence and a bit assignment code from the estimated unified spectral sequence. A quantization step size obtainer 11 obtains a quantization step size from the estimated unified spectral sequence and the bit assignment sequence.