Audio Compression With Delta Coding for Low-Overhead Decompression
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Solution Overview
Problem
Existing audio compression and decompression technologies require significant computational resources and memory, making them unsuitable for low-power devices with limited storage and processing capabilities.
Innovation Solution
The proposed solution involves an audio compression algorithm that achieves low-overhead decompression by using quantization, difference coding, and variable block sizes, resulting in a compressed audio file that requires minimal computational and memory resources for decompression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing audio compression and decompression technologies are used, then audio quality can be maintained, but computational resources and memory requirements become too high for low-power devices
Solution Approach 1:
The audio data is divided into blocks of samples, with each block processed independently through quantization and difference coding. This segmentation allows the decompression algorithm to work on small, manageable units rather than large continuous streams, reducing memory requirements and computational overhead at any given moment.
Solution Approach 2:
The patent applies quantization to transform continuous audio sample values into discrete quantized values, and uses difference coding to represent changes between consecutive samples. These parameter transformations reduce the data precision requirements during decompression, enabling low-power devices to process audio with limited computational resources while maintaining acceptable audio quality.
2Quantity of substance
If high compression ratios are achieved, then storage requirements are reduced, but decompression complexity and resource requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for reconstruction by computing difference values between consecutive samples and storing only these differences along with quantization parameters. This extraction approach achieves high compression ratios by eliminating redundant data while keeping the decompression process simple, as the receiver only needs to perform basic addition operations to reconstruct the original audio samples.
3Use of energy by moving object
If low-power operation is implemented, then energy consumption is reduced, but processing capability and memory availability are limited
Solution Approach 1:
The decompression algorithm is designed to be self-sufficient, requiring minimal external resources. Each block of audio samples can be decompressed independently using only the quantization parameters and difference values stored in the compressed data, without needing complex lookup tables or extensive memory. This self-service approach enables low-power devices with limited processing capability to perform audio decompression effectively.
Data Source
AI summary
A system comprising an audio compression device is provided. The audio compression device receives a plurality of pulse code modulated (PCM) samples, performs a quantization of the plurality of the PCM samples, and determines a plurality of time windows for the plurality of quantized PCM samples. The audio compression device further determines a first number of delta bits and a first sample count for a first time window of a plurality of time windows, where the first time window includes a first group of time-domain consecutive samples according to the first sample count. The audio compression device encodes the sample into the first number of delta bits based on a difference from a previous sample to generate a first stream of delta bits, and encodes the first sample count and the first number of delta bits in a corresponding first header for the first time window.