Programmable Image Sensor ROI Control for Power and Data Precision
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Solution Overview
Problem
Existing image sensors lack dynamic programmability and efficient resource allocation, leading to suboptimal performance in power consumption, frame rate, and data processing due to lack of control over pixel cell operations and inefficient data transmission.
Innovation Solution
An integrated controller dynamically programs the image sensor based on image data, determining regions of interest and adjusting power, quantization resolution, and bit length for subsets of pixel cells, enabling fine-grained control and co-optimization with the image processor to reduce resource waste and improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all pixel cells operate at high resolution and frame rate continuously, then image quality is maintained, but power consumption increases and resource waste occurs
Solution Approach 1:
The pixel array is divided into multiple independently controllable groups or regions, allowing selective activation of only those pixel cells needed for current imaging tasks. This segmentation enables the system to maintain high resolution where required while powering down unused pixels, directly resolving the contradiction between image quality and power consumption.
Solution Approach 2:
The system dynamically adjusts the operational state of pixel cells based on real-time imaging requirements, object detection results, and scene complexity. This dynamic reconfiguration allows the image sensor to transition between high-resolution and low-power modes adaptively, maintaining image quality when needed while reducing power consumption during static or low-detail scenes.
2Productivity
If the image sensor operates at high frame rate continuously, then motion capture accuracy is improved, but data processing burden increases and latency increases
Solution Approach 1:
The system extracts and processes only the most relevant frame data or key motion information rather than transmitting and processing every captured frame. By selecting critical frames based on motion detection or scene changes, the system maintains high frame rate capability while reducing the actual processing burden and latency.
Solution Approach 2:
The system processes a subset of captured frames or processes only specific regions of interest within frames at full resolution. This partial processing approach allows the sensor to operate at high frame rates for capture while reducing the effective processing load to manageable levels, thereby reducing latency.
3Measurement precision
If high quantization resolution is used for all pixel data, then data precision is improved, but data transmission bandwidth requirements increase
Solution Approach 1:
Different quantization resolutions are applied to different regions or groups of pixel data based on their importance and scene characteristics. Critical regions maintaining high quantization resolution for precision, while less important areas use lower resolution, thereby reducing overall data transmission volume while preserving necessary data precision.
4Device complexity
If the image sensor uses fixed programming signals for all operations, then device complexity is reduced, but adaptability to different imaging scenarios deteriorates
Solution Approach 1:
The image sensor incorporates a unified control architecture that can dynamically generate and apply different programming signals for various imaging scenarios. This universal controller handles multiple functions including region selection, resolution adjustment, frame rate control, and power management, providing scenario adaptability without proportionally increasing overall device complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the overall imaging system performance by optimizing power usage, reducing latency, and improving data precision, particularly in applications requiring high frame rates and resolution, such as augmented and virtual reality.
Implementation Method 1
Each pixel cell may include a photodiode to sense light by converting photons into charge (e.g., electrons or holes)
Data Source
Figure 1A
Figure 1B
Figure 2
AI summary
In one example, an apparatus comprises: an image sensor comprising an array of pixel cells, each pixel cell including a photodiode and circuits to generate image data, the photodiodes formed in a first semiconductor substrate; and a controller formed in one or more second semiconductor substrates that include the circuits of the array of pixel cells, the first and second semiconductor substrates forming a stack and housed within a semiconductor package. The controller is configured to: determine whether first image data generated by the image sensor contain features of an object; based on whether the first image data contain the features of the object, generate programming signals for the image sensor; and control, based on the programming signals, the image sensor to generate second image data.