Audio Frame Coding by Pulse Sample Segmentation
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Solution Overview
Problem
Existing dynamic range coding methods 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 reduced compression efficiency.
Innovation Solution
The method involves sorting samples in a data frame into pulse and non-pulse samples based on their distribution, encoding each type separately using appropriate coding modes, and multiplexing the results to reduce the overall number of bits required for encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dynamic range coding is used for data frames with wide dynamic range, then all samples are encoded uniformly, but the number of bits required for encoding increases significantly
Solution Approach 1:
The patent divides samples into two categories: pulse samples (those with values unequal to V0 and V1) and non-pulse samples (those with values equal to V0 or V1). This segmentation allows different encoding strategies to be applied to different sample types, reducing the overall bit requirement while maintaining encoding accuracy.
Solution Approach 2:
The patent applies different encoding qualities to different sample types. Non-pulse samples are encoded with fewer bits since they only need to represent two possible values (V0 or V1), while pulse samples receive more detailed encoding. This local differentiation optimizes the overall bit usage based on the actual information content of each sample type.
2Adaptability or versatility
If the number of bits per sample is increased to handle wide dynamic range, then encoding coverage is improved, but compression efficiency deteriorates
Solution Approach 1:
The patent changes the encoding parameters based on the statistical distribution of sample values. By identifying the two most frequently occurring values (V0 and V1) and using them as reference points, the encoding scheme adapts to the specific characteristics of each data frame, achieving both wide coverage and high compression efficiency.
Solution Approach 2:
The patent applies full encoding precision only to pulse samples (those needing detailed representation), while using reduced precision for non-pulse samples. This partial application of encoding resources maintains adaptability for diverse signal characteristics while significantly improving compression efficiency.
Data Source
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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.