Predictive Signal Padding for Clearer Boundary Extrapolation
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Solution Overview
Problem
Conventional padding methods in signal processing lose boundary information, leading to blurred images, ringing effects, and poor performance in downstream tasks due to the lack of integration with predictive coding theory.
Innovation Solution
A signal processing method involving predictive padding, which includes extracting task-related and noise signals using a first kernel function, padding these with predicted extrapolation and constant value signals respectively, and merging them to generate a predictive padding signal, followed by a second kernel function to produce a downstream task input signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If zero padding is used to maintain output signal size and improve spectrum resolution, then the spectrum resolution is improved, but boundary information is lost and ringing effects occur
Solution Approach 1:
The patent applies preliminary action by using predictive coding to pre-calculate the boundary values before convolution operations. The predictive padding is computed in advance based on the relationship between boundary and internal pixels, so that when convolution occurs, the boundary information is already prepared and preserved, preventing information loss while maintaining spectrum resolution.
Solution Approach 2:
The patent introduces predictive coding as an intermediary mechanism between the original signal and the padding process. This intermediary uses the correlation between pixels to generate predicted boundary values that serve as a bridge, preserving boundary information while enabling the zero-padding technique to maintain its spectrum resolution benefits without causing ringing effects.
2Stability of the object's composition
If conventional padding methods are used to complete data boundaries, then the data boundary is completed, but downstream task performance deteriorates due to boundary information loss
Solution Approach 1:
The patent implements feedback by using the actual boundary pixel values to correct and refine the predictive padding. The system compares the predicted boundary values with actual values and uses this feedback to improve the accuracy of boundary completion, ensuring that downstream tasks receive high-quality data with preserved boundary information, thus maintaining reliability.
Solution Approach 2:
The patent applies preliminary action by pre-computing predictive padding values before downstream tasks are executed. This preliminary preparation ensures that boundary information is preserved in advance, allowing downstream tasks to operate on complete and accurate data without suffering from boundary information loss, thereby maintaining high task performance.
3Shape
If zero padding is applied to avoid missing edge images, then the edge image is preserved, but image clarity deteriorates due to ringing artifacts
Solution Approach 1:
The patent applies parameter changes by modifying the padding values from fixed zero values to dynamically predicted values based on pixel correlations. This change in the padding parameter (from constant zero to variable predicted values) preserves edge images while avoiding the ringing artifacts that occur with traditional zero padding, thereby maintaining image clarity.
Solution Approach 2:
The patent introduces predictive coding as an intermediary that generates optimized padding values. This intermediary process calculates predicted boundary values that smoothly continue the image content, serving as a mediator between the need for edge preservation and the need to avoid ringing artifacts, thus maintaining both edge integrity and image clarity.
Data Source
AI summary
A signal processing method of predictive padding is used to perform predictive padding on an original signal and generate a predictive padding signal accordingly. The signal processing method for predictive padding includes a first kernel function process, a predictive padding process, and a signal merging process. The first kernel function process operates the original signal with a first kernel function to extract a task-related signal and a noise signal. The predictive padding process pads one end of the task-related signal with a predicted extrapolation signal and pads one end of the noise signal with a constant value signal. The signal merging process merges the task-related signal padded with the predicted extrapolation signal and the noise signal padded with the constant value signal to generate the predictive padding signal.