Audio Encoding Bit Allocation via Lossless Energy Scaling
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Solution Overview
Problem
Existing audio encoding methods face challenges in increasing the number of bits allocated to encode actual spectral components while reducing the bits for energy information within a limited bit range without increasing complexity or deteriorating audio quality.
Innovation Solution
The method involves determining a lossless encoding mode for quantization coefficients, either infinite-range or finite-range, and allocating bits accordingly to encode energy and spectral coefficients efficiently, allowing for increased bits in spectral encoding without compromising quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of bits allocated to encode energy information is reduced, then the number of bits available for encoding actual spectral components increases, but the audio quality may deteriorate
Solution Approach 1:
The patent applies parameter changes by introducing a scaling factor that adjusts the energy values before quantization. This allows the energy information to be represented with fewer bits while maintaining the perceptual quality through intelligent parameter transformation. The scaling factor modifies the energy parameters dynamically based on the audio characteristics, enabling efficient bit allocation without quality loss.
Solution Approach 2:
The patent implements dynamics by making the bit allocation adaptive rather than static. The encoder dynamically determines the number of bits to allocate to energy information based on the actual audio content and spectral characteristics. This dynamic adaptation allows the system to use fewer bits for energy when possible while preserving quality when necessary, resolving the contradiction between bit reduction and quality maintenance.
2Measurement precision
If lossless encoding mode is used for energy quantization coefficients, then encoding precision is improved, but the number of bits required increases
Solution Approach 1:
The patent transforms the energy quantization coefficients through scaling before lossless encoding. This parameter change allows the coefficients to be represented more efficiently, reducing the number of bits required while maintaining lossless precision. The scaling operation compresses the dynamic range of energy values, enabling more compact representation without losing information.
3Manufacturing precision
If more bits are allocated to spectral components, then encoding accuracy improves, but the complexity of the encoding system increases
Solution Approach 1:
The patent segments the encoding process into distinct stages: energy calculation, scaling, quantization, and spectral coefficient encoding. This segmentation allows each stage to be optimized independently, managing complexity while achieving high encoding accuracy. By separating the energy processing from spectral encoding, the system can allocate bits efficiently without increasing overall system complexity.
Data Source
AI summary
A lossless encoding method is provided that includes determining a lossless encoding mode of a quantization coefficient as one of an infinite-range lossless encoding mode and a finite-range lossless encoding mode; encoding the quantization coefficient in the infinite-range lossless encoding mode in correspondence with a result of the lossless encoding mode determination; and encoding the quantization coefficient in the finite-range lossless encoding mode in correspondence with a result of the lossless encoding mode determination.