Audio Encoder SNR Offset for Transcoder Complexity Reduction
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Solution Overview
Problem
Current audio encoding and decoding systems face challenges in efficiently converting audio content between different codec systems, such as Dolby Digital and Dolby Digital Plus, due to high computational complexity, which increases costs and resource requirements for transcoders and encoders.
Innovation Solution
The system employs an audio encoder that determines control parameters to reduce computational complexity by performing iterative bit allocation and quantization, allowing for efficient conversion of audio content from one codec system to another, including the use of Modified Discrete Cosine Transform and floating-point encoding to optimize bitstream generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If an updated audio codec system (first audio codec system) is used to provide additional features and improved coding quality, then audio quality and functionality are improved, but computational complexity of the encoder increases due to the need to insert control parameters for transcoder support
Solution Approach 1:
The encoder performs preliminary bit allocation and quantization operations to generate control parameters (such as SNR offset values) that are inserted into the bitstream. These pre-computed parameters enable the transcoder to perform conversion with reduced computational complexity, resolving the contradiction by shifting the computational burden to the encoding stage while simplifying the transcoding stage.
2Adaptability or versatility
If a transcoder is used to convert audio content from the first audio codec system to the second audio codec system, then compatibility with legacy devices is improved, but computational complexity of the transcoder increases
Solution Approach 1:
Control parameters (such as SNR offset values and bit allocation information) act as intermediaries between the first audio codec system and the second audio codec system. These parameters are inserted into the bitstream by the encoder and used by the transcoder to perform efficient conversion, enabling compatibility with legacy devices while significantly reducing the computational complexity of the transcoder.
3Productivity
If iterative bit allocation and quantization processes are performed to generate control parameters, then conversion efficiency is improved, but the encoding process becomes more complex
Solution Approach 1:
The encoder performs iterative bit allocation and quantization processes that involve changing parameters such as SNR offset values, bit allocation vectors, and quantization matrices. These parameter changes enable the generation of optimized control parameters that improve conversion efficiency, with the iterative processes systematically adjusting parameters to achieve optimal bit allocation for the target codec system.
Data Source
AI summary
The present document relates to audio encoding/decoding. In particular, the present document relates to a method and system for reducing the complexity of a bit allocation process used in the context of audio encoding/decoding. An audio encoder (300) configured to encode an audio signal according to a first audio codec system is described. The audio encoder (300) comprises a transform unit (302) configured to determine a set of spectral coefficients (312) based on the audio signal. Furthermore, the encoder (300) comprises a floating-point encoding unit (304) configured to determine a set of scale factors and a set of scaled values (314), based on the set of spectral coefficients (312); and to encode the set of scale factors to yield a set of encoded scale factors (313).