Digital Image Scaling Using Context-Based Pixel Classification
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Solution Overview
Problem
Existing digital image scaling techniques often result in blur and jagged edges when resizing images, particularly when increasing resolution, due to sub-optimal interpolation methods that fail to accurately capture local image characteristics and are computationally intensive.
Innovation Solution
A method that classifies pixels into context classes using feature vectors derived from differences between adjacent pixels, allowing for efficient interpolation with a reduced number of context classes and single interpolation filters, enabling high-quality non-integer scaling while minimizing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classification-based approaches with multiple context classes are used, then image quality is improved, but device complexity and computational cost increase
Solution Approach 1:
The patent applies local quality by using a reduced set of context classes (e.g., edge, smooth, textured regions) with locally adaptive interpolation filters tailored to each class. Instead of using numerous complex classification categories, the invention focuses on capturing essential local image characteristics through simplified context classification, assigning appropriate interpolation strategies to each context type to maintain image quality while reducing computational burden.
Solution Approach 2:
The invention changes parameters by reducing the number of context classes from traditional large numbers (e.g., 10-20 classes) to a smaller set (e.g., 3-5 classes), and by optimizing interpolation filter parameters for each context class. This parameter reduction maintains adequate image quality while significantly lowering computational complexity and device requirements for consumer equipment.
2Manufacturing precision
If higher order interpolations are used, then manufacturing precision is improved, but productivity decreases due to computational intensity
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing interpolation filter coefficients for each context class during system initialization or manufacturing. These pre-computed filters are then directly applied during runtime without requiring complex real-time calculations, thus maintaining high spatial detail accuracy while achieving fast processing speeds suitable for consumer devices.
Solution Approach 2:
The invention substitutes complex mechanical-like higher order interpolation algorithms with simplified filter-based approaches. By replacing computationally intensive mathematical operations with pre-computed convolution filters, the system achieves comparable or superior image quality with significantly reduced computational requirements, enabling real-time processing in consumer equipment.
3Ease of operation
If pixel replication or bilinear interpolation is used, then ease of operation is improved, but manufacturing precision deteriorates with blur and jagged edges
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into distinct context-based segments. Different interpolation strategies are applied to different image regions based on their characteristics (edges, smooth areas, textured regions). This segmentation allows the system to use simple operations where appropriate while applying more sophisticated methods only where needed, maintaining image quality without excessive complexity.
Solution Approach 2:
The invention creates a universal interpolation framework that handles multiple image types and scaling scenarios through a single unified system. The context-classification approach with pre-computed filters provides a multi-functional solution that adapts to various image characteristics and scaling requirements, replacing the need for multiple specialized algorithms while maintaining ease of implementation.
Data Source
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AI summary
A method and apparatus for changing the spatial resolution of a digital image are disclosed. A feature vector (Y) for a pixel (101) in the digital image (102) is obtained. The pixel (101) is classified so that the pixel (101) is allocated to one or more context classes by comparing the feature vector (Y) of the pixel (101) with a prototype of each of said context classes. Each context class has one or more interpolation filters associated therewith. The pixel (101) is filtered using the one or more interpolation filters associated with the one or more context classes to which said pixel (101) is allocated, thereby to generate one or more pixels of the final digital image. The feature vector (Y) for the pixel (101) is a function of differences between selected adjacent pairs of pixels in the first digital image, at least one of said adjacent pairs of pixels in the first digital image not including said pixel (101).