Motion-Assisted Data Enhancement via Pixel Trace Segmentation
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Solution Overview
Problem
Digital video and audio data are often degraded by noise during transmission and processing, leading to a need for improved methods to enhance data quality.
Innovation Solution
The method involves motion-assisted data enhancement techniques, including averaging and non-linear processing of pixel values across frames, motion estimation, and the use of histograms to exclude noisy values, which helps in reducing noise and improving data quality by estimating original pixel values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion estimation and trace construction are used to follow pixels across frames, then noise reduction is improved, but computational complexity increases
Solution Approach 1:
The image is divided into multiple frames, and pixel traces are constructed by segmenting and following individual pixels or groups of pixels across these frames. This segmentation allows noise reduction to be applied selectively to moving objects while preserving their motion characteristics, resolving the contradiction between noise reduction effectiveness and computational complexity by processing only relevant pixel segments rather than entire frames.
Solution Approach 2:
The patent applies noise reduction selectively to pixels that are part of moving objects (identified through trace construction) rather than processing all pixels uniformly. This partial action approach focuses computational resources on regions where noise reduction is most beneficial, improving the noise reduction effect while avoiding unnecessary computation on static or irrelevant areas.
2Measurement precision
If pixel values from multiple frames are averaged, then data quality is improved, but processing time increases
Solution Approach 1:
Pixel traces are constructed in advance by following pixels across multiple frames before the actual averaging operation is performed. This preliminary trace construction organizes the data structure efficiently, allowing the averaging operation to proceed more quickly by operating on pre-organized trace data rather than raw frame data, thus reducing overall processing time while maintaining data quality.
Solution Approach 2:
The averaging operation is applied selectively only to pixels that are part of identified moving objects (those with constructed traces), rather than averaging all pixels from multiple frames. This partial application of averaging reduces the total number of operations required, decreasing processing time while still achieving improved data quality for the relevant moving objects.
3Measurement precision
If histograms are used to exclude noisy values, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
Histograms are constructed and analyzed locally for each pixel trace or group of pixels rather than for the entire image. This local approach allows noisy values to be excluded selectively based on the specific characteristics of each trace, improving measurement precision for moving objects while reducing computational complexity by limiting histogram operations to small local regions rather than the whole image.
Data Source
AI summary
A method of enhancing data. A trace may be determined from a target data point in a target frame and a respective data point in an adjacent frame. At least an approximate value of the target data point may be determined from the trace.