Audio Frame Coding with Pulse Separation for Bit Reduction
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Solution Overview
Problem
Existing dynamic range coding schemes for audio signals are inefficient when dealing with data frames having a wide dynamic range, as they require a large number of bits for encoding, leading to poor compression efficiency.
Innovation Solution
The method involves analyzing the distribution of sample values in a data frame to identify pulse and non-pulse samples, encoding them separately using different coding modes, and multiplexing the results to reduce the overall number of bits required for encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dynamic range coding is used for audio signals with wide dynamic range, then the coding can handle the full range of sample values, but the number of bits required for encoding each sample increases significantly
Solution Approach 1:
The patent segments the data frame into multiple sub-frames based on the distribution of sample values. Each sub-frame is encoded separately with its own dynamic range calculation, allowing the system to handle wide dynamic range signals while reducing the average number of bits per sample by focusing precision only where needed.
Solution Approach 2:
The patent applies different coding precision to different regions of the signal by calculating dynamic range separately for each sub-frame. This local quality approach ensures that areas with small dynamic range use fewer bits while areas requiring high precision maintain adequate bit allocation, resolving the contradiction between handling wide ranges and minimizing bit usage.
2Ease of manufacture
If uniform bit allocation is used for all samples in a data frame, then the encoding process is simple, but compression efficiency deteriorates when dynamic range varies across the frame
Solution Approach 1:
The patent divides the data frame into multiple sub-frames and calculates dynamic range separately for each segment. This segmentation enables adaptive bit allocation that improves compression efficiency while maintaining relatively simple encoding logic within each sub-frame, balancing simplicity and efficiency.
Solution Approach 2:
The patent introduces dynamic sub-frame segmentation where the number and size of sub-frames can be adjusted based on the signal characteristics. This dynamic approach allows the encoding system to adapt to varying dynamic ranges across different portions of the audio signal, improving compression efficiency without significantly complicating the encoding process.
Data Source
AI summary
A coding method, a decoding method, a coding apparatus, and a decoding apparatus are disclosed herein. A coding method includes: obtaining a value of each sample of an input data frame; determining pulse samples and non-pulse samples in the input data frame according to the distribution of values of samples of the input data frame; encoding the determined pulse samples in the input data frame in a first coding mode to obtain a first data stream; encoding the determined non-pulse samples in the input data frame in a second coding mode to obtain a second data stream; and multiplexing the first data stream and the second data stream to obtain an output coded data stream of the input data frame. Compared with the prior art, the technical solution under the present disclosure reduces the number of bits required for encoding the entire data frame is reduced, and improves the compression efficiency of the data frame with a wide dynamic range.