Image Subsampling Algorithm Selection by Rate
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Solution Overview
Problem
Current image subsampling algorithms face challenges in balancing image quality with performance overhead, necessitating a method to reduce performance overhead while maintaining image quality.
Innovation Solution
An image subsampling method that determines the subsampling interval for a target image and applies either a first or second subsampling algorithm based on the interval, using linear interpolation, regional average interpolation, or window function algorithms to generate a subsampled image, with optional sharpening calculations to adjust pixel values within the color space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high-performance subsampling algorithm is used, then image quality is improved, but performance overhead increases
Solution Approach 1:
The patent changes the parameter of subsampling rate to select different algorithms. When subsampling rate is less than 2, linear interpolation is used; when subsampling rate is 2 or greater, regional average interpolation or window function algorithms are used. This parameter-based selection resolves the contradiction by matching algorithm complexity to the actual quality requirements of each scenario.
Solution Approach 2:
The patent segments the subsampling algorithm selection into multiple intervals based on subsampling rate. Different algorithm intervals are defined: linear interpolation for low subsampling rates, regional average interpolation for medium rates, and window function algorithms for high rates. This segmentation allows optimal performance by applying the right level of complexity only when needed.
2Use of energy by stationary object
If a simple subsampling algorithm is used, then performance overhead is reduced, but image quality deteriorates
Solution Approach 1:
The patent uses subsampling rate as a threshold parameter to determine algorithm selection. For subsampling rates less than 2, simple linear interpolation suffices and provides acceptable quality. For subsampling rates of 2 or greater, more complex regional average interpolation or window function algorithms are applied to maintain quality. This parameter-driven approach ensures quality is maintained only when complexity is justified.
Solution Approach 2:
The patent implements dynamic algorithm selection based on runtime conditions (subsampling rate). The system adapts its processing complexity dynamically rather than using a fixed algorithm, allowing it to optimize between quality and performance based on the specific subsampling requirements of each image processing task.
Data Source
AI summary
An image subsampling method and an apparatus, which relate to the technical field of image processing. The method comprises: acquiring a target image and a subsampling rate; determining a subsampling interval to which the subsampling rate belongs; when the subsampling rate belongs to a first subsampling interval, performing subsampling on the target image by using a first subsampling algorithm; and when the subsampling rate belongs to a second subsampling interval, performing subsampling on the target image by using a second subsampling algorithm; the second subsampling interval is greater than the first subsampling interval; the performance overhead of the second subsampling algorithm is greater than the performance overhead of the first subsampling algorithm; and at an identical subsampling rate, the image quality of a subsampled image obtained by the second subsampling algorithm is higher than the image quality of a subsampled image obtained by the first subsampling algorithm.