Audio Compression Mapping for Low-Range Fidelity and Easy Decoding
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Solution Overview
Problem
Existing audio compression methods compromise fidelity, especially in the lower dynamic range of audio signals, leading to poor quality playback when decompressed, and are resource-intensive for decompression processes.
Innovation Solution
Implementing a dual-mapping scheme where sampled audio values are divided into lower and upper ranges, with fixed mapping for the lower range and adaptive mapping for the upper range, allowing for efficient compression and decompression that preserves fidelity and reduces resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If audio data is compressed by reducing bit depth (e.g., from 24-bit to 8-bit), then storage efficiency is improved, but audio fidelity is lost especially in lower dynamic ranges
Solution Approach 1:
The dynamic range of audio signals is segmented into lower and upper ranges. The lower range (which contains most of the dynamic range information) is encoded with fixed mapping to preserve fidelity, while the upper range is encoded with adaptive mapping. This segmentation allows different portions of the audio signal to be treated differently, maintaining quality where it matters most while achieving compression overall.
Solution Approach 2:
Different mapping strategies are applied to different portions of the audio dynamic range. The lower range receives fixed mapping with higher precision to maintain fidelity, while the upper range uses adaptive mapping. This local differentiation of encoding quality ensures that the most perceptually important portions of the audio signal are preserved while achieving overall compression.
2Quantity of substance
If traditional compression algorithms are used, then storage efficiency is improved, but decompression becomes resource-intensive
Solution Approach 1:
The mapping relationships (fixed and adaptive) are pre-computed and stored in lookup tables during the encoding phase. During decompression, the system simply performs table lookups rather than performing complex real-time calculations. This preliminary preparation of mapping data significantly reduces the computational resources required during playback on resource-constrained devices.
Solution Approach 2:
Instead of performing complex decompression calculations, the system uses pre-computed lookup tables that contain the mapping relationships. The decompression process copies data from these tables rather than computing it in real-time, significantly reducing the processing burden on playback devices.
3Quantity of substance
If bit depth is reduced from 24-bit to 8-bit, then storage space is nearly halved, but the lower dynamic range loses fidelity
Solution Approach 1:
The dynamic range is segmented into lower and upper ranges, with the lower range receiving special treatment through fixed mapping to preserve precision. This segmentation allows the system to allocate encoding resources differently across the dynamic range, ensuring the lower range maintains fidelity despite overall compression.
Solution Approach 2:
The lower dynamic range is assigned fixed mapping with higher precision to maintain measurement accuracy, while the upper range uses adaptive mapping. This local quality differentiation ensures that the perceptually most important portion of the audio signal (the lower dynamic range) maintains precision even when overall bit depth is reduced.
Data Source
AI summary
Systems, methods, and software are disclosed herein for compressing audio data. In an implementation, sampled values of an audio signal have a dynamic range. A division of the dynamic range, into at least a lower range and an upper range, is identified based on a fixed mapping of a lower portion of the sampled values to a subset of quanta in a set of quanta having a depth less than a depth of the sampled values. Then an adaptive mapping of an upper portion of the sampled values to a remaining subset of quanta in the set of quanta is also identified, based at least on a dimension of the upper range. The fixed mapping is used to encode the lower portion of the sampled values based, while the adaptive mapping is used to encode the upper portion of the sampled values based on the adaptive mapping.