Video Blurring via Diagonal Pixel Sampling
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Solution Overview
Problem
Traditional image blurring methods are resource-intensive and often result in noticeable artifacts, particularly when applying blur to digital images or video content, as they require sampling pixels along horizontal or vertical axes, which can lead to ghosting or other visual distortions.
Innovation Solution
The method involves selectively sampling pixels along a diagonal axis within a sliding window using a specified noise pattern, performing initial and subsequent convolution passes to reduce the number of samples needed, and dynamically adapting the sampling based on available processing resources and image content, thereby minimizing artifacts and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional horizontal or vertical pixel sampling is used for blurring, then the blurring process is simple to implement, but the computational resources required are excessive and artifacts are noticeable
Solution Approach 1:
The patent transforms the traditional horizontal/vertical sampling approach by introducing diagonal sampling at 45-degree angles. This dimensional change in sampling orientation reduces the number of pixels that need to be processed while maintaining blur quality, directly addressing the computational efficiency problem without sacrificing ease of implementation
2Ease of manufacture
If traditional horizontal or vertical pixel sampling is used for blurring, then the implementation is straightforward, but noticeable artifacts such as ghosting are produced
Solution Approach 1:
By sampling pixels along diagonal axes at 45-degree angles instead of traditional horizontal or vertical orientations, the patent eliminates the ghosting and artifact problems that occur with axis-aligned sampling. This dimensional change in sampling orientation prevents the formation of noticeable artifacts while keeping the implementation straightforward
3Manufacturing precision
If a 100×100 pixel area is blurred using traditional methods, then complete coverage is achieved, but 100×100 samples per pixel are required which is highly resource intensive
Solution Approach 1:
The diagonal sampling approach processes fewer pixels to achieve the same blur coverage. By sampling along diagonal lines at 45-degree angles, the algorithm covers the 100×100 pixel area effectively while requiring significantly fewer than 100×100 samples per pixel, thus reducing computational resource consumption while maintaining complete blur coverage
4Productivity
If the number of samples is reduced in traditional blurring algorithms, then computational resources are saved, but artifacts become noticeable and distracting
Solution Approach 1:
The patent demonstrates that by changing the sampling orientation to diagonal axes, it is possible to reduce the number of samples taken while simultaneously avoiding artifacts. The diagonal sampling pattern at 45-degree angles provides a computational efficiency improvement without introducing the ghosting or distracting artifacts that plague reduced-sample traditional methods
Data Source
AI summary
The disclosed computer-implemented method includes determining that an image is to be blurred. The image has multiple pixels arranged along horizontal and/or vertical axes. The method next includes identifying a boundary size for a sliding window within which pixel values are to be sampled from the image and sampling, from pixels that lie on an axis that is diagonal relative to the horizontal/vertical axis of the image, various pixel values from within the boundary of the sliding window. The pixels sampled along the diagonal angle within the sliding window are selected according to a specified noise pattern. The method further includes performing an initial convolution pass on the pixels surrounding the sampled pixels to blur at least some of the pixels surrounding the sampled pixels, and then presenting the blurred image. Various other methods, systems, and computer-readable media are also disclosed.