Scalable Lossless Audio Codec MSB LSB Encoding
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Solution Overview
Problem
Current lossless audio codecs are inefficient in handling high bit-rate requirements, often necessitating alteration of audio files to fit media constraints, which reduces audio content and is computationally intensive, and lacks precise control over information removal.
Innovation Solution
A scalable lossless audio codec and authoring tool that separates audio data into most significant bit (MSB) and least significant bit (LSB) portions, encodes them differently, and selectively discards LSBs to conform to bit rate constraints without altering the audio files, ensuring efficient bitstream management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lossless audio encoding is used to maintain high audio quality, then audio fidelity is improved, but bit rate increases and compression efficiency deteriorates
Solution Approach 1:
The audio bitstream is segmented into conforming frames that meet media bit rate constraints and non-conforming frames that exceed them. Each frame is independently analyzed and processed, allowing selective application of lossless encoding only where bit rate permits, while maintaining overall audio fidelity through strategic placement of lossless segments throughout the bitstream.
Solution Approach 2:
Different quality levels are applied locally to different portions of the audio bitstream. Conforming frames receive full lossless encoding for maximum fidelity, while non-conforming frames undergo selective bit depth reduction or filtering to reduce bit rate. This local differentiation optimizes the balance between overall audio quality and bit rate constraints.
2Quantity of substance
If audio files are altered to reduce information content and fit media constraints, then bit rate compliance is improved, but audio quality deteriorates and the process becomes computationally intensive
Solution Approach 1:
The system performs preliminary analysis of the entire audio bitstream to identify conforming and non-conforming frames before applying any quality reduction. This advance planning allows selective application of loss reduction only where necessary, preserving audio quality in conforming frames while ensuring bit rate compliance in non-conforming frames through targeted modifications.
Solution Approach 2:
The system dynamically changes encoding parameters such as bit depth and frequency bandwidth selectively applied to non-conforming frames. By adjusting these parameters only where needed to achieve bit rate compliance, the system maintains maximum audio quality in conforming frames while ensuring overall bit rate constraints are met.
3Quantity of substance
If selective bit depth reduction is applied to non-conforming frames, then bit rate is reduced to meet constraints, but audio information content is lost
Solution Approach 1:
The system applies partial loss reduction by selectively reducing bit depth only in non-conforming frames where bit rate exceeds constraints, while maintaining full bit depth in conforming frames. This partial application of loss reduction minimizes overall information loss while achieving necessary bit rate compliance, rather than applying uniform reduction across the entire bitstream.
Data Source
AI summary
An audio codec losslessly encodes audio data into a sequence of analysis windows in a scalable bitstream. This is suitably done by separating the audio data into MSB and LSB portions and encoding each with a different lossless algorithm. An authoring tool compares the buffered payload to an allowed payload for each window and selectively scales the losslessly encoded audio data, suitably the LSB portion, in the non-conforming windows to reduce the encoded payload, hence buffered payload. This approach satisfies the media bit rate and buffer capacity constraints without having to filter the original audio data, reencode or otherwise disrupt the lossless bitstream.