Audio Spectral Postprocessing for Scalable Transform Compatibility
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Solution Overview
Problem
Existing audio encoding and decoding technologies, such as MP3 and AAC, face inefficiencies in scalability due to incompatible transformation algorithms, leading to high computational overhead and inefficient bit allocation in forming extension layers for lossless compression.
Innovation Solution
A postprocessing method using weighted additions of spectral values from one transformation algorithm to align with another, such as integer MDCT, allowing for efficient formation of extension layers without the need for additional decoding and encoding steps, using permanently programmed weighting factors to minimize data rate and maintain quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different transformation algorithms (e.g., MDCT vs. integer MDCT) are used in base layer and extension layer, then scalability and flexibility are improved, but computational overhead and complexity increase due to incompatibility
Solution Approach 1:
A postprocessing device acts as an intermediary between the base layer transformation output and the extension layer transformation input. This device transforms the output of the first transformation algorithm to be compatible with the second transformation algorithm, enabling scalability while maintaining computational efficiency. The postprocessing device converts spectral values from the base layer into a format that can be directly used by the extension layer, eliminating the need for separate decoding and encoding operations.
2Manufacturing precision
If separate decoding and encoding steps are performed for extension layers, then transformation compatibility is improved, but processing time and computational resources increase
Solution Approach 1:
The postprocessing transformation is performed in advance on the base layer spectral values before they are passed to the extension layer. By pre-transforming the spectral values to match the second transformation algorithm's requirements, the system eliminates the need for separate decoding and encoding steps later, significantly reducing processing time while maintaining transformation compatibility.
3Measurement precision
If lossless compression is implemented through extension layers, then audio quality is improved, but bit rate and data volume increase
Solution Approach 1:
The system changes the transformation parameters between base layer and extension layer, using different transformation algorithms optimized for different purposes. The base layer uses a standard transformation for efficient compression, while the extension layer uses a different transformation with appropriate postprocessing to achieve lossless reconstruction. This parameter change enables high-quality audio representation without uniformly increasing the entire system's bit rate.
Data Source
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AI summary
For postprocessing spectral values which are based on a first transformation algorithm for Converting the audio Signal into a spectral representation, first a sequence of blocks of the spectral values representing a sequence of blocks of samples of the audio signal are provided. Hereupon, a weighted addition of spectral values of the sequence of blocks of spectral values is performed in order to obtain a sequence of blocks of postprocessed spectral values, wherein the combination is performed such that for calculating a postprocessed spectral value for a frequency band and a time duration a spectral value of the sequence of blocks for the frequency band and the time duration and a spectral value for another frequency band or another time duration are used, wherein the combination is further performed such that such weighting factors are used that the postprocessed spectral values are an approximation to the spectral values as they are obtained by converting the audio signal into a spectral representation using a second transformation algorithm which is different from the first transformation algorithm. The postprocessed spectral values are in particular used for a difference formation within a scalable encoder or for an addition within a scalable decoder, respectively.