Multi-Camera Image Selection Using Visual Quality Evaluation
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Solution Overview
Problem
Conventional camera systems struggle to automatically select images with good visual quality from multiple cameras, relying heavily on operator skill and failing to consider viewer preferences, leading to suboptimal image selection and high manual workload in live streaming and highlight scene creation.
Innovation Solution
An information processing apparatus that automatically selects images for streaming by acquiring images from multiple cameras, evaluating their visual quality using a visual-quality evaluation value, and selecting candidate images based on these evaluations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual image selection is performed by operators, then image selection can be performed with consideration of visual quality, but the manual workload increases and operational complexity increases
Solution Approach 1:
The system performs automatic image selection by having the image capturing apparatus itself evaluate visual quality metrics and select optimal images without external operator intervention. The apparatus uses built-in evaluation units to assess images based on multiple criteria including subject state, camera movement, and composition, thereby serving itself rather than requiring manual operation.
Solution Approach 2:
The manual mechanical process of operator-based image selection is replaced with an automated electronic evaluation system. The system uses computational algorithms to assess visual quality, analyze subject state, and determine optimal images automatically, substituting human operators with machine-based evaluation and selection mechanisms.
2Productivity
If automated image selection is implemented without visual quality evaluation, then manual workload is reduced, but the visual quality of selected images deteriorates
Solution Approach 1:
The system incorporates visual quality evaluation as feedback in the automated selection process. The evaluation unit continuously assesses images based on multiple criteria including subject state, camera movement, and composition quality, using this feedback information to guide the automatic selection of optimal images for output or further processing.
Solution Approach 2:
The system evaluates and selects images based on multiple parameters including subject state, camera movement characteristics, composition quality, and other visual metrics. By changing from simple automated selection to multi-parameter evaluation-based selection, the system maintains high visual quality while achieving automation efficiency.
3Measurement precision
If multiple evaluation criteria are used for image selection, then visual quality improves, but the system complexity increases
Solution Approach 1:
The evaluation system is divided into separate functional units, each responsible for evaluating specific criteria such as subject state, camera movement, and composition quality. This segmentation allows the complex multi-criteria evaluation to be performed through coordinated operation of simpler, specialized modules, managing system complexity while maintaining comprehensive evaluation accuracy.
Data Source
AI summary
An information processing apparatus includes a first acquiring unit configured to acquire a plurality of images respectively obtained by a plurality of image capturing devices, a second acquiring unit configured to acquire, for each of the plurality of images, a visual-quality evaluation value indicating a degree of visual quality of the image, and a selecting unit configured to select one or more candidate images from among the plurality of images based on the visual-quality evaluation value, the one or more candidate images each being a candidate of an image to be used for viewing.