Dual Exposure Control in Camera Systems

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Solution Overview

Problem

Current camera systems lack the ability to provide independent, real-time control over shadow and highlight exposure levels before image capture, limiting artistic and scene-specific control for users.

Innovation Solution

A live-view camera interface with adjustable sliders or control features allows for separate real-time adjustments to shadow and highlight exposure levels, using total exposure time (TET) and gain multipliers to generate accurate previews and improve image quality by adjusting shutter speed, aperture, and image sensitivity before capture.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If global exposure adjustment is used, then the entire image brightness is controlled, but independent control over shadow and highlight areas is lost

Engineering Contradiction:
Improveexposure controlVSAvoidscene-specific control
Core Design Contradiction:
Ease of operationVSAdaptability or versatility

Solution Approach 1:

The patent divides the exposure control into separate regions by implementing first and second exposure settings that are independently adjustable. The first exposure setting controls at least one of shadow areas and midtone areas, while the second exposure setting controls at least one of highlight areas and midtone areas. This segmentation allows users to independently adjust different regions of the image without affecting the entire image uniformly.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The patent applies different exposure characteristics to different regions of the image. By providing separate exposure controls for shadow, midtone, and highlight areas, the system enables localized optimization of each region's brightness and detail. This allows the shadow areas to have different exposure characteristics than the highlight areas, achieving scene-specific control where needed.

Inventive Principle:
Principle #3Local quality

2Manufacturing precision

If post-processing adjustments are used, then image quality can be improved, but time is lost and noise/artifacts increase

Engineering Contradiction:
Improveimage qualityVSAvoidprocessing time
Core Design Contradiction:
Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent applies exposure adjustments during the image capture process itself rather than after capture. By setting different exposure values for different regions before the image is taken, the system performs the necessary adjustments in advance, during the actual capture operation. This eliminates the need for time-consuming post-processing and avoids introducing noise and artifacts that would result from adjusting an already-captured image.

Inventive Principle:
Principle #10Preliminary action

3Adaptability or versatility

If multiple exposure settings are applied simultaneously, then shadow and highlight control is achieved, but device complexity increases

Engineering Contradiction:
Improveexposure controlVSAvoidexposure control system
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements a unified image capture device that can apply multiple exposure settings simultaneously to different regions. The image capture device is configured to capture an image using both the first exposure setting (for shadow and midtone areas) and the second exposure setting (for highlight and midtone areas), integrating multiple functions into a single device operation rather than requiring separate devices or complex post-processing pipelines.

Inventive Principle:
Principle #6Universality (Multi-functionality)

Data Source

PatentEP4018641B1Dual exposure control in a camera system
Publication Date: 2024.01.24 GOOGLE LLC
  • EP4018641B1 patent drawingFigure 1
  • EP4018641B1 patent drawingFigure 2A~2C
  • EP4018641B1 patent drawingFigure 3A~3C

AI summary

Apparatus and methods related to applying lighting models to images of objects are provided. A neural network can be trained to apply a lighting model to an input image. The training of the neural network can utilize confidence learning that is based on light predictions and prediction confidence values associated with lighting of the input image. A computing device can receive an input image of an object and data about a particular lighting model to be applied to the input image. The computing device can determine an output image of the object by using the trained neural network to apply the particular lighting model to the input image of the object.