Hierarchical Sharpness Evaluation for Image Asset Organization
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Solution Overview
Problem
Existing image processing techniques struggle to effectively organize and evaluate the quality of image assets, often presenting poorly composed or blurry media at the forefront due to reliance on coarse categorization methods like time or date of capture, rather than assessing image sharpness.
Innovation Solution
A system that downsamples images to generate multiple versions, evaluates blocks for blurriness, and calculates a blurriness score using a neural network-based sharpness analyzer, identifying and classifying blurry content, and designating key frames based on the least blurry image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If automated tools rely on coarse categorization methods like time or date of capture, then organization of assets is simplified, but image quality assessment becomes inaccurate and poorly composed images are incorrectly identified as key images
Solution Approach 1:
The image is divided into multiple blocks that are processed independently through the neural network. Each block is evaluated for sharpness separately, allowing detailed local assessment while maintaining computational efficiency. This segmentation enables accurate identification of blurry regions without requiring complex global analysis.
Solution Approach 2:
The patent transforms the image assessment from traditional coarse temporal categorization to a multi-dimensional approach using neural network analysis across different spatial blocks and frequency domains. By adding this analytical dimension, the system achieves precise sharpness measurement while maintaining automated organization capabilities.
2Device complexity
If traditional categorization methods are used to organize image assets, then processing complexity is reduced, but the quality of presented images deteriorates with blurry images appearing at the forefront
Solution Approach 1:
A neural network-based sharpness analyzer is introduced as an intermediary component between image input and organization output. This intermediary performs automated sharpness assessment and generates quality scores, enabling reliable image selection without requiring complex manual intervention or sacrificing processing efficiency.
Solution Approach 2:
The system performs self-assessment of image sharpness through automated neural network analysis. The analyzer independently evaluates each image block, generates blurriness scores, and identifies key images without external intervention, maintaining both low complexity and high reliability in the organization process.
3Measurement precision
If detailed block-by-block analysis is performed to accurately assess sharpness, then measurement precision improves, but computational complexity and processing time increase
Solution Approach 1:
The image is divided into multiple blocks that are processed independently through the neural network. Each block is evaluated for sharpness separately, allowing detailed local assessment while maintaining computational efficiency. This segmentation enables accurate identification of blurry regions without requiring complex global analysis.
Solution Approach 2:
The system focuses computational resources on analyzing only the necessary blocks and regions that contribute to overall sharpness assessment. By performing partial analysis on strategically selected blocks rather than exhaustive processing of every pixel, the system achieves high measurement precision with reduced computational complexity.
Data Source
AI summary
Techniques are disclosed for estimating quality of images in an automated fashion. According to these techniques, a source image may be downsampled to generate at least two downsampled images at different levels of downsampling. Blurriness of the images may be estimated starting with a most-heavily downsampled image. Blocks of a given image may be evaluated for blurriness and, when a block of a given image is estimated to be blurry, the block of the image and co-located blocks of higher resolution image(s) may be designated as blurry. Thereafter, a blurriness score may be calculated for the source image from the number of blocks of the source image designated as blurry.