Adaptive Interpolation for Image Up-Sampling
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Solution Overview
Problem
Current image processing methods for up-sampling low-resolution images require significant computational resources, leading to high demands on computing power and inefficiencies in resource utilization, especially when dealing with simple image content.
Innovation Solution
An image processing method that calculates an interpolation feature for pixel blocks, performing first or second interpolation based on the complexity of the image content, reducing computational resource consumption by selecting the appropriate interpolation method for different complexities, thereby improving flexibility and efficiency in image resolution adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If spatial enlargement algorithm is used for up-sampling low-resolution images, then image resolution is increased and display effect is improved, but computational complexity increases significantly
Solution Approach 1:
The patent applies different interpolation methods to different pixel blocks based on their content characteristics. Edge pixels use one interpolation method while non-edge pixels use another, optimizing computational resources by matching the interpolation complexity to the local image content requirements rather than applying a uniform high-complexity method to the entire image.
Solution Approach 2:
The patent changes the interpolation method parameter based on the detected edge characteristics of pixel blocks. By detecting whether a pixel block contains edges and selecting appropriate interpolation methods accordingly, the system dynamically adjusts computational complexity to match actual image content needs, reducing overall computational burden while maintaining image quality.
2Manufacturing precision
If high-complexity interpolation method is applied to all pixel blocks, then up-sampling quality is maximized, but computational resource consumption increases
Solution Approach 1:
The patent divides the image into different regions (edge and non-edge pixel blocks) and applies appropriate interpolation methods to each region. This localized approach ensures high up-sampling quality where needed (edge regions) while using computationally lighter methods in regions where simplicity is sufficient (non-edge regions), thereby optimizing the balance between quality and resource consumption.
Solution Approach 2:
The patent applies the more computationally intensive interpolation method only partially - specifically to edge pixel blocks where it is most needed - rather than applying it excessively to all pixel blocks. This selective application maintains necessary up-sampling quality while significantly reducing overall computational resource consumption compared to universal high-complexity interpolation.
Data Source
AI summary
Various image processing features are described. An interpolated pixel block of a first image may be used to generate a second image. Several types of interpolation may be available for obtaining the interpolated pixel block, and a particular type of interpolation may be determined based on various conditions, such as whether an interpolation feature of a first pixel block in the first image satisfies a feature determination condition. Different interpolation may be performed on the first pixel block according to complexity of image content in the first pixel block, which effectively reduces the computational complexity of up-sampling, avoids the computational resource waste caused by high computational resource consumption interpolation in the case of simple image content, and further reduces computational complexity.