Adaptive Image Resolution Scaling for Selective Downsampling
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Solution Overview
Problem
Imaging devices capture images at a set resolution based on pixel density, but varying distances and distortions lead to areas with different frequency components, necessitating high spatial sampling for lossless recovery, which is inefficient and resource-intensive.
Innovation Solution
Utilize depth sensors, auto-focus parameters, and geometric distortion models to identify image portions with diminished signal information, applying lower-scale processing to reduce computational resources without compromising image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high spatial sampling is applied to all image areas, then lossless recovery of high spatial frequency components is achieved, but processing resources and computational complexity increase significantly
Solution Approach 1:
The patent applies different processing resolutions to different spatial regions of the image based on local frequency characteristics. High spatial sampling is applied only to in-focus regions containing high spatial frequency components, while out-of-focus regions are processed at lower resolution. This resolves the contradiction by maintaining measurement precision where needed while improving overall processing efficiency.
Solution Approach 2:
The image is segmented into multiple regions with different focus characteristics using depth information and auto-focus parameters. Each segment is then processed at an appropriate resolution level, allowing lossless recovery of high frequencies in relevant segments while reducing computational load in less critical segments.
2Reliability
If uniform high-resolution processing is applied to the entire image, then image quality is maintained, but computational resources and processing time are wasted on areas with diminished signal information
Solution Approach 1:
The processing resolution is dynamically adjusted for different image regions based on real-time depth sensor data and auto-focus parameters. Regions with diminished signal information (out-of-focus areas) are automatically processed at lower resolution, reducing processing time while maintaining overall image quality in critical regions.
Solution Approach 2:
Instead of applying full high-resolution processing to the entire image, the system applies high-resolution processing only partially to regions where it is actually needed (in-focus areas with high spatial frequency content). This partial action maintains image quality where necessary while significantly reducing total processing time.
3Productivity
If adaptive resolution scaling is implemented, then processing resources are optimized, but device complexity increases due to multiple processing paths
Solution Approach 1:
Depth information and auto-focus parameters are obtained in advance before the main image processing occurs. This preliminary action allows the system to pre-identify regions requiring high-resolution processing, enabling efficient resource allocation without adding complex real-time decision-making logic during the processing stage.
Solution Approach 2:
Depth sensor data and auto-focus parameters serve as intermediary information that bridges the gap between the captured image and the appropriate processing resolution. These intermediaries guide the adaptive resolution scaling algorithm, simplifying the overall system architecture by providing clear criteria for resolution selection without requiring complex analysis of the image content itself.
Data Source
AI summary
Example methods, apparatuses, and/or articles of manufacture are disclosed that may implement, in whole or in part, techniques to process portions of an image frame according to a level of diminished signal information. Portions of an image frame experiencing diminished signal information may be sampled a lower rate/more sparsely to reduce impacts to downstream image processing resources.