Sparse Representation for High-Resolution Video Editing
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Solution Overview
Problem
Existing methods for editing and propagating high-resolution video images consume excessive memory and time, struggling to maintain fidelity in limited memory spaces, and are inefficient in processing large-scale images.
Innovation Solution
A method utilizing sparse representation techniques to acquire highly sparse samples of high-resolution video images, editing and propagating only these samples rather than all pixels, reducing memory consumption and ensuring excellent visual fidelity through reconstruction coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If overall optimization methods are used to maintain similarity among all pixels during editing and propagation, then the fidelity of the result is improved, but the memory consumption increases excessively
Solution Approach 1:
The patent segments the image data into sparse samples that capture the essential characteristics of the image content. Instead of processing all pixels, only a small number of sparse samples are selected and processed, dramatically reducing memory consumption while preserving the ability to maintain fidelity through the sparse representation model.
Solution Approach 2:
The patent transforms the image representation from dense pixel data to sparse sample data, changing the fundamental parameters of data storage and processing. This parameter change enables the system to maintain image fidelity with significantly reduced memory requirements by using reconstruction coefficients to recover the full image from sparse samples.
2Manufacturing precision
If all pixels are processed during editing and propagation, then the visual effect and fidelity are maintained, but the processing time becomes too long
Solution Approach 1:
The patent extracts only the essential sparse samples from the full image data that contain the key visual information. By taking out and processing only these critical samples rather than all pixels, the processing time is dramatically reduced while the extracted sparse samples are sufficient to maintain the visual effect and fidelity of the final result.
Solution Approach 2:
The patent applies partial action by processing only a small subset of sparse samples rather than the complete set of all pixels. This partial processing approach is sufficient to achieve the desired editing and propagation effects with much faster processing speed, as the sparse samples contain the essential information needed for faithful reconstruction.
3Manufacturing precision
If high-resolution video images are processed using existing techniques, then the quality of the result is maintained, but the memory space required exceeds that of ordinary computers
Solution Approach 1:
The patent segments high-resolution video images into sparse samples that capture the essential visual information. This segmentation approach allows the system to process and edit high-resolution images while using only a fraction of the memory space, as only the sparse samples and their reconstruction coefficients need to be stored rather than the complete high-resolution pixel data.
Solution Approach 2:
The patent creates a sparse copy representation of the high-resolution image data. Instead of working with the full high-resolution pixels, the system creates and processes a sparse sample copy that contains the essential information, enabling high-quality editing and propagation on ordinary computers with limited memory resources.
Data Source
AI summary
This invention provides a method of sparse representation of contents of high-resolution video images which supports content editing and propagation. It mainly comprises five steps which are: (1) to input the original images or videos of high resolution to summarize the characteristic information of their pixels; (2) to acquire the highly sparse samples of the original images or videos through the sparse representation technique; (3) to reconstruct each pixel of the input images or videos with a small number of the original sparse samples to calculate a coefficient of reconstruction; (4) to edit and propagate the original sparse samples to yield a result of new sparse samples; (5) to generate a result of final images or videos of high resolution according to the result of sparse samples and the coefficient of reconstruction. This invention only edits and propagates the highly sparse samples rather than all the information of pixels. Thus, the memory consumption can be greatly reduced so as to possibly process the images or videos of high resolution in a very small memory space. It is very potential to be widely applied in the fields of image processing, computer vision and augmented reality technique.