Saliency Map Camera Control for Real-Time Focus and Exposure
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Solution Overview
Problem
Existing image capture technologies struggle to accurately adjust camera settings such as autofocus, autoexposure, and auto-white balance in real-time to enhance image quality by focusing on regions that attract viewer attention, often leading to suboptimal results due to limitations in post-processing techniques and reliance on non-real-time data.
Innovation Solution
The use of saliency maps generated in real-time to determine the relative saliency of image regions, allowing for immediate adjustments to camera settings like autofocus, autoexposure, and auto-white balance based on the likelihood of attracting viewer gaze, utilizing neural networks trained with eye-tracking data for enhanced image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If camera settings are adjusted manually or with simple automation, then device complexity remains low, but image quality and viewer engagement are suboptimal
Solution Approach 1:
The patent replaces manual mechanical camera adjustment with an automated AI-based saliency detection system. The machine learning model analyzes image content to automatically determine focus regions, exposure settings, and white balance parameters, eliminating the need for manual intervention while achieving superior image quality that adapts to viewer attention patterns.
Solution Approach 2:
The system dynamically changes camera parameters (aperture, shutter speed, ISO, focus distance) based on real-time saliency map analysis. By adjusting these parameters according to the detected salient regions and viewer attention patterns, the system optimizes image quality for each specific scene without requiring complex manual reconfiguration.
2Productivity
If real-time saliency-based adjustment is implemented, then viewer engagement and image relevance improve, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary saliency detection and camera setting optimization during the preview mode before the actual image capture. By pre-calculating the saliency map and determining optimal parameters in advance, the system eliminates processing delays during the actual shot, maintaining real-time responsiveness while achieving high image quality.
Solution Approach 2:
The patent implements selective processing by focusing computational resources only on analyzing salient regions rather than processing the entire image uniformly. This targeted approach reduces overall processing time while maintaining high accuracy in identifying viewer attention patterns and optimizing camera settings for the most important visual elements.
3Measurement precision
If multiple saliency maps for different depths are generated, then depth accuracy and focus precision improve, but computational complexity and processing steps increase
Solution Approach 1:
The system segments the image into multiple depth layers by generating separate saliency maps for different depth ranges. This segmentation allows the system to independently analyze and optimize focus settings for foreground, midground, and background regions, achieving precise depth measurement while managing computational complexity through structured processing.
Solution Approach 2:
The patent adds the depth dimension to traditional 2D saliency analysis by generating three-dimensional saliency representations. This dimensional extension enables the system to identify salient objects at different distances from the camera, improving focus precision and depth measurement accuracy through multi-layered analysis rather than single-plane processing.
Data Source
AI summary
A device for image capture comprises a memory and one or more processors coupled to the memory and configured to: receive, during a preview mode or a recording, a first image, generate a first saliency map indicative of relative saliency of different regions within the first image, wherein the relative saliency of the different regions is indicative of a likelihood of attracting viewer gaze, generate one or more additional images based on manipulating pixels in the first image, generate one or more additional saliency maps indicative of relative saliency of different regions within the one or more additional images, and determine, during the preview mode or the recording, a camera setting based on the first saliency map and the one or more additional saliency maps.