AON Camera Exposure Control with Auxiliary ROI Frames
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Solution Overview
Problem
Existing Always-On (AON) digital cameras face challenges in accurately controlling exposure parameters, particularly in low illumination conditions and high dynamic range scenes, leading to loss of information due to noise, pixel saturation, or motion blur, especially when objects of interest are in peripheral or high contrast regions.
Innovation Solution
A method that determines a region of interest (ROI) in the first frame and, if not detectable, uses auxiliary exposure correction information to acquire an auxiliary frame, from which exposure parameters are calculated to correct subsequent frames, optimizing exposure for the ROI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional AEC algorithms use fixed target convergence level and histogram statistics from center area, then the exposure control is simple to implement, but the exposure accuracy for objects in peripheral regions or high contrast scenes deteriorates
Solution Approach 1:
The patent applies local quality by determining exposure parameters specifically for the region of interest (ROI) rather than using a fixed global target convergence level. The system calculates a target convergence level adaptively based on the luminance statistics of the detected ROI, allowing different parts of the image to have different exposure optimization goals. This resolves the contradiction by maintaining simple implementation through automated ROI detection while achieving high exposure accuracy for objects in any region including peripheral areas and high contrast scenes.
2Measurement precision
If AEC algorithms optimize exposure for dominant objects, then the exposure of main subjects is improved, but the performance in high dynamic range scenes without dominant objects or with peripheral objects deteriorates
Solution Approach 1:
The patent applies preliminary action by performing ROI detection and luminance statistics calculation before final exposure parameter determination. The system pre-processes the image to identify objects of interest and computes their luminance characteristics in advance, then uses this information to set appropriate target convergence levels. This allows the AEC algorithm to adapt to various scene types including high dynamic range scenes without dominant objects, as the preliminary ROI detection can identify multiple potential subjects or scene characteristics that guide subsequent exposure optimization.
3Productivity
If AON camera continuously acquires frames for scene analysis, then the scene analysis capability is improved, but the power consumption increases
Solution Approach 1:
The patent applies self-service by enabling the AON camera to perform exposure control using its own continuously acquired frames without requiring additional dedicated resources. The system utilizes the luminance information from the continuous frame stream to determine exposure parameters for subsequent frames, making the camera self-sufficient for both scene analysis and exposure control functions. This resolves the contradiction by maintaining continuous scene analysis capability while avoiding the need for separate high-power analysis modules, thus controlling power consumption.
Data Source
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AI summary
Method for exposure control including acquiring at least one first frame by a first image sensor; determine a region of interest, ROI, in the first frame; if no ROI in the first frame can be determined, determine auxiliary exposure correction information from the first frame and acquire an auxiliary frame on the basis of the auxiliary exposure correction information; and determine exposure parameters from the auxiliary frame and applying the exposure parameters to a subsequent frame for exposure correction of the subsequent frame.