Tempered Auto-Adjusting Image Editing Operation
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Solution Overview
Problem
Current media editing applications lack automatic image adjustment controls, requiring users to manually adjust sliders or enter values for image properties, leading to inconvenience and potential inaccuracies in image editing.
Innovation Solution
A media editing application performs automatic exposure adjustment by multiplying color or luminance values of image pixels by a computed multiplier, using a method that determines this value based on a histogram analysis to identify the necessary adjustment, such as darkening or brightening the image, and accounts for prior adjustments to the image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automatic exposure adjustment is implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system performs automatic exposure adjustment by analyzing the image histogram and computing the optimal multiplier value independently, without requiring user intervention. The application automatically identifies distance to black and distance to white, computes the multiplier, and applies the adjustment, allowing the system to serve itself in the image adjustment task.
Solution Approach 2:
The system changes the exposure parameter by computing a multiplier value based on histogram analysis. The multiplier is derived from image data characteristics (distance to black and white) and applied to adjust the exposure parameter automatically, transforming the raw image into an adjusted image with optimized exposure settings.
2Manufacturing precision
If manual adjustment controls are provided, then manufacturing precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system provides feedback by analyzing the image histogram and using the statistical distribution of pixel luminance values to determine the optimal exposure adjustment. The feedback loop involves computing distance to black and white from the histogram, calculating the multiplier based on these distances, and applying the adjustment to achieve accurate exposure correction.
Solution Approach 2:
The patent replaces the mechanical slider adjustment system with an automated computational system. Instead of requiring users to manually move sliders and observe changes, the system uses algorithmic processing of image data to automatically compute and apply the optimal exposure adjustment, substituting mechanical interaction with digital signal processing.
3Productivity
If automatic adjustment is performed, then productivity is improved, but measurement precision may deteriorate
Solution Approach 1:
The system performs preliminary analysis by computing the image histogram and identifying key characteristics (distance to black and white) before applying the exposure adjustment. This preliminary action of analyzing image statistics allows the system to prepare the optimal multiplier value in advance, ensuring both speed and accuracy in the adjustment process.
Solution Approach 2:
The system applies a computed multiplier that may be partial (less than full adjustment) or excessive (more than minimal adjustment) based on the histogram analysis. The multiplier is determined to achieve the desired exposure correction while accounting for the specific characteristics of the image, allowing flexible adjustment that adapts to different image conditions.
Data Source
AI summary
Some embodiments provide a novel method for tempering an adjustment of an image to account for prior adjustments to the image. The adjustment in some embodiments is an automatic exposure adjustment. The method performs an operation for a first adjustment on a first set of parameters (e.g., saturation, sharpness, luminance). The method compares the first set of parameters to a second set of parameters to produce a third set of parameters that expresses the difference between the first adjustment and a second adjustment. The method performs a third operation to produce an adjusted image. The first set of parameters quantify a set of prior adjustments to the image by an image capturing device when the image was captured in some embodiments. The second set of parameters is a set of target parameters. The third set of parameters specify the tempered adjustment of the image.