Attention-Level Image Processing for Digital Camera Output
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Solution Overview
Problem
Existing image processing methods for digital cameras output multiple images based on fixed processing conditions, leading to unnecessary images and complicated user selection, as they do not account for the type of imaged subject or the photographer's attention.
Innovation Solution
An image processing method that generates object regions within an image, calculates an attention level for each region based on its imaged state, and sets output conditions to prioritize images based on the calculated attention levels, allowing for user-preferred processed image options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple processed images are output using fixed processing conditions, then various image options are provided to the user, but unnecessary images are outputted and image selection becomes complicated
Solution Approach 1:
The image is divided into multiple object regions based on detected objects, and processing is performed separately for each region. This segmentation allows the system to focus on specific objects of interest rather than processing the entire image uniformly, thereby reducing the number of unnecessary processed images while maintaining versatility in handling different object types.
Solution Approach 2:
Different image processing conditions are applied to different object regions based on their specific characteristics. For example, portrait mode processing is applied to human faces, while landscape mode is applied to scenery. This local quality approach ensures that each object receives appropriate processing, reducing the need for multiple global processing options and simplifying user selection.
2Productivity
If fixed processing conditions are used for all images, then processing is simple and fast, but the processing does not match the photographer's attention or subject type
Solution Approach 1:
The system performs preliminary object detection and region segmentation before applying image processing. By pre-identifying objects and their regions of interest, the system can quickly determine the appropriate processing conditions without requiring multiple trial processing passes, thus maintaining high processing efficiency while achieving accurate, subject-appropriate processing results.
Solution Approach 2:
The system dynamically changes processing parameters based on the detected object type and region characteristics. Different processing parameters (such as sharpness, contrast, color adjustment) are automatically selected according to the object class, enabling accurate processing that matches photographer intent while maintaining efficient single-pass processing through automated parameter selection.
Data Source
AI summary
When a processed image obtained by performing image processing on an original image is presented, an option desired by a user is presented. A plurality of object regions is detected from an original image to detect the imaged state of each of the object regions when the original image was obtained. Then, an attention level for each object region is calculated based on the imaged state of the object region, and an output condition and an image processing condition are set based on the calculated attention level. Thereafter, a processed image is generated by performing image processing according to the image processing condition, and the processed image is outputted according to the output condition.