Image Sensor Sampling Density Adjustment
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Solution Overview
Problem
Conventional image sampling techniques are limited in varying sampling density from frame to frame, leading to bandwidth constraints that hinder high-quality image rendition, especially in high-speed image applications like high frame rate video recording.
Innovation Solution
Implementing a method and system that evaluates captured images to dynamically adjust sampling densities across image sensor arrays, allowing for variable sampling based on spatial frequency content or perceptual significance, such as faces, to optimize data transfer and improve image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If uniform sampling is used from frame to frame to reduce sample size, then data transfer bandwidth is reduced, but image quality deteriorates because important regions are not prioritized
Solution Approach 1:
The patent applies local quality by varying the sampling density across different regions of the image sensor array based on the evaluated image content. Regions with higher spatial frequency or perceptual significance (such as faces or detailed areas) are sampled at higher densities, while regions with lower importance are sampled at lower densities. This non-uniform sampling approach allows the system to maintain image quality in critical areas while reducing overall data volume to manage bandwidth constraints.
2Measurement precision
If higher sampling density is applied to all regions, then image quality is maintained, but data transfer bandwidth is exceeded
Solution Approach 1:
The system evaluates the captured image to identify regions with higher spatial frequency content or perceptual significance, then applies higher sampling densities only to those specific regions. This localized high-quality sampling ensures that important visual information is preserved while reducing the sampling density in less important areas, thereby controlling the total data volume to fit within bandwidth limitations.
Solution Approach 2:
The image sensor array is segmented into multiple regions based on the evaluated image content, with each region assigned a different sampling density. This segmentation allows the system to treat different parts of the image differently, applying high sampling density only where necessary and lower sampling density where acceptable, thus optimizing the balance between image quality and data volume.
3Device complexity
If conventional fixed sampling techniques are used, then implementation is simple, but adaptability to varying image content and bandwidth constraints is limited
Solution Approach 1:
The patent implements dynamic sampling by adjusting the sampling density for each frame based on the evaluated image content. Rather than using a fixed sampling pattern, the system dynamically determines the optimal sampling allocation for each captured image, allowing adaptation to varying scene complexity, spatial frequency content, and perceptual importance. This dynamic approach enables the system to optimize the balance between image quality and bandwidth usage for each specific frame.
Solution Approach 2:
The system employs feedback by evaluating the captured image to determine the sampling allocation for subsequent frames. The evaluation of spatial frequency content and perceptual significance provides feedback that guides the sampling decision, allowing the system to adapt future sampling patterns based on the actual image content. This feedback mechanism enables intelligent adjustment of sampling density to match the specific requirements of each frame.
Data Source
AI summary
Systems and methods according to the present invention provide techniques which adjust the sampling scheme associated with an image sensor in order to provide high quality renditions, e.g., printed or displayed outputs. The sampling scheme can be varied on a frame by frame basis (or less frequently) to output more samples from the image sensor for regions which are predicted to contain more perceptually significant information and to output fewer samples for regions which are predicted to be less perceptually significant. Various parameters can be used as proxies for perceptual significance. For example, in images which contain people, faces can be characterized as more perceptually significant and regions which are predicted to contain faces can be sampled more densely than regions which are not predicted to contain faces. Alternatively, the spatial frequency content of data captured by an image sensor can be measured to identify regions of higher and lower spatial frequency content. Regions with higher spatial frequency content will typically contain perceptually more significant image features, e.g., edges and textures, than regions having lower spatial frequency content. Accordingly, the image sensor can be programmed to sample the former more densely.