Scale Factor Prediction Residuals for Audio Compression
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Solution Overview
Problem
Existing audio compression techniques are inefficient in representing and decoding scale factor information, leading to high bit rates and quality trade-offs, particularly in high-quality audio content processing.
Innovation Solution
The techniques involve selecting from multiple scale factor prediction modes, spectral resolutions, and reordering scale factor prediction residuals to optimize bit rate and quality, including smoothing scale factor amplitudes and using flexible prediction methods for improved entropy encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional audio compression techniques are used to represent scale factor information, then the audio quality can be maintained, but the bit rate becomes excessively high
Solution Approach 1:
The patent applies parameter changes by transforming scale factor data into prediction residuals that exhibit different statistical characteristics. By changing the representation from raw scale factors to residuals relative to predicted values, the entropy of the data is reduced, enabling more efficient entropy encoding while maintaining the same reconstruction quality.
Solution Approach 2:
The patent substitutes direct representation of scale factor information with a prediction-based approach. Instead of encoding the actual scale factor values, the system encodes the difference between actual and predicted values (residuals), which have lower variance and can be compressed more efficiently using entropy coding techniques.
2Measurement precision
If high-quality audio content is processed with detailed scale factor information, then the perceived signal quality improves, but the storage and transmission costs increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for high-quality reconstruction by encoding prediction residuals instead of complete scale factor data. The prediction component is derived from previously decoded information, and only the residual (difference) needs to be transmitted, separating the redundant predictable portion from the essential unpredictable portion.
Solution Approach 2:
The patent performs preliminary prediction of scale factor values using previously decoded audio data before the actual encoding step. This preliminary action allows the system to anticipate what the scale factors will be, so that only the deviations from these predictions need to be encoded, reducing the amount of data that requires storage and transmission.
3Productivity
If scale factor information is compressed to reduce bit rate, then transmission efficiency improves, but the reconstruction quality deteriorates
Solution Approach 1:
The patent employs feedback by using previously decoded scale factor information to predict current scale factors. The decoder maintains state information from prior decoding steps and uses this feedback to reconstruct the current scale factors, ensuring that the reconstruction quality matches the encoder's output while transmitting fewer bits.
Solution Approach 2:
The patent applies dynamics by adapting the prediction approach based on the temporal and spectral characteristics of the audio signal. The prediction residuals are reordered and entropy encoded with varying precision depending on the signal properties, allowing the system to dynamically adjust the representation to maintain quality while optimizing compression.
Data Source
AI summary
Techniques and tools for representing, coding, and decoding scale factor information are described herein. For example, during encoding of scale factors, an encoder uses one or more of flexible scale factor resolution selection, spatial prediction of scale factors, flexible prediction of scale factors, smoothing of noisy scale factor amplitudes, reordering of scale factor prediction residuals, and prediction of scale factor prediction residuals. Or, during decoding, a decoder uses one or more of flexible scale factor resolution selection, spatial prediction of scale factors, flexible prediction of scale factors, reordering of scale factor prediction residuals, and prediction of scale factor prediction residuals.