Segmented Signal Encoding with Translated Predictors and Error Control
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Solution Overview
Problem
Existing signal encoders introduce prediction error buildup due to discontinuities when using the last predicted value of the previous segment as the seed value for the next segment, leading to undesirable offsets and reduced encoding quality.
Innovation Solution
The signal encoder uses the last predicted value of the previous segment as the seed value for the next segment, applies each linear prediction model in its own translated axis system, and clusters predictor model parameters and prediction errors to reduce data transmission and storage requirements, allowing for efficient error correction and improved signal fidelity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the last sample value of the previous segment is used as the seed value for the next segment, then the encoding process is simple and continuous, but prediction error builds up and introduces discontinuities leading to undesirable offsets
Solution Approach 1:
The patent introduces an intermediary transformation process where the seed value is not used directly but is transformed through a mathematical relationship (exponential scaling with factor alpha) before being applied as the second seed value. This mediator transformation allows the system to maintain continuity in the encoding process while compensating for prediction errors that would otherwise accumulate, thus resolving the contradiction between operational simplicity and prediction accuracy.
2Manufacturing precision
If predictor model parameters are transmitted in full precision, then signal reconstruction accuracy is maintained, but data transmission bandwidth increases
Solution Approach 1:
The patent transforms the predictor model parameters by applying a mathematical transformation (exponential scaling with factor alpha) to the seed values and adjusting the prediction errors accordingly. This parameter change allows the system to maintain reconstruction accuracy while working with transformed parameters that can be more efficiently encoded and transmitted, thus reducing the effective data volume while preserving signal fidelity.
Solution Approach 2:
The patent discards the full-precision original predictor model parameters and instead transmits transformed parameters (scaled seed values and adjusted prediction errors). The original accuracy is recovered at the decoder through the inverse transformation process, allowing efficient transmission while maintaining reconstruction quality. This discarding and recovering approach reduces bandwidth requirements without sacrificing accuracy.
Data Source
AI summary
A signal encoder divides the signal into segments and uses prediction models to approximate the samples of each segmentEach local prediction model, each applicable to one segment, is applied in its own translated axis system within the segment and the offset is given by the last predicted value for the previous segment. When the signal is reasonably continuous, it alleviates the need to parameterize the offset for each local predictor model as each local predictor model can build on this last predicted sample value of the previous segment.The encoder as a consequence doesn't suffer from a build up of error even though the offset is not transmitted but instead the last predicted value of the last sample of the previous segment is used. Prediction errors are obtained for the approximated samples and transmitted to the decoder, together with the predictor model parameters and seed value to allow accurate reconstruction of the signal by the decoder.