Viewport-Based Metadata Estimation for Image Processing Efficiency
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Solution Overview
Problem
Processing per frame metadata in image processing algorithms is resource-intensive and often results in sub-par outcomes.
Innovation Solution
Subdivide metadata into smaller subdivisions and generate metadata estimations based on viewport information, using algorithms like bilinear interpolation and area-based weighted sum, to optimize processing and improve results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If per frame metadata is processed using traditional methods, then complete metadata processing is achieved, but resource consumption increases and processing efficiency decreases
Solution Approach 1:
The patent divides the image frame into multiple tiles and processes metadata for each tile independently rather than processing the entire frame at once. This segmentation allows parallel processing of different regions, improving overall processing efficiency while reducing the computational resources needed for each individual processing operation.
2Loss of time
If per frame metadata is processed using traditional methods, then all metadata is processed, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing metadata for each tile separately before the actual image processing operation. This allows the system to quickly retrieve and apply only the necessary metadata during processing, significantly reducing processing time while maintaining high throughput by having metadata ready in advance.
3Use of energy by moving object
If viewport-based estimation is used, then resource consumption is reduced, but metadata processing completeness may be compromised
Solution Approach 1:
The patent applies local quality by differentiating processing levels based on the viewport. High-quality complete metadata processing is applied only to tiles within the visible viewport, while tiles outside the viewport use estimation techniques. This approach maintains processing quality where it matters most (visible areas) while reducing resource consumption in less critical areas.
Data Source
AI summary
In one implementation, a method is performed for generating metadata estimations based on metadata subdivisions. The method includes: obtaining an input image; obtaining metadata associated with the input image; subdividing the metadata into a plurality of metadata subdivisions; determining a viewport relative to the input image based on at least one of head pose information and eye tracking information; generating one or more metadata estimations by performing an estimation algorithm on at least a portion of the plurality of metadata subdivisions based on the viewport; and generating an output image by performing an image processing algorithm on the input image based on the one or more metadata estimations.


