Noise Statistics Circuitry for Artifact-Free Image Scaling
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Solution Overview
Problem
Existing image processing techniques often introduce artifacts like jagged edges and blurriness when scaling image data for higher resolution displays, affecting perceived image quality.
Innovation Solution
The use of noise statistics, differential statistics, and angle detection to identify optimal interpolation angles for directional scaling, combined with enhancement circuitry for improving image resolution and reducing noise, allows for scaling and enhancement of image data while maintaining image definition and reducing artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data is scaled to higher resolution using conventional techniques, then the resolution is improved, but image artifacts such as jagged edges and blurriness are introduced
Solution Approach 1:
The patent applies preliminary action by performing noise statistics gathering and angle detection on the original image data before scaling operations. The system pre-calculates noise characteristics and identifies dominant edge orientations, then uses this pre-analyzed information to guide the scaling process, thereby reducing artifacts like jagged edges and blurriness that would otherwise be introduced by conventional scaling techniques
Solution Approach 2:
The patent applies local quality by differentiating between noise regions and actual image content through statistical analysis. The system calculates noise statistics locally across different regions of the image and uses angle detection to identify local edge orientations. This localized analysis allows the scaling algorithm to apply different processing strategies to different regions, preserving important image features while suppressing noise, thus reducing artifacts while maintaining resolution
2Object-generated harmful factors
If noise reduction techniques are applied to image data, then the perceived image quality is improved, but image definition and sharpness may be reduced
Solution Approach 1:
The patent applies local quality by performing localized noise statistics gathering and angle detection on different regions of the image. The system identifies regions with high noise characteristics versus regions containing important image features such as edges and textures. By analyzing local patterns and comparing against noise statistics, the system can selectively apply noise reduction only where appropriate while preserving definition and sharpness in regions where it would be harmful
Solution Approach 2:
The patent applies feedback by using the gathered noise statistics and angle detection results to dynamically adjust the noise reduction process. The system continuously monitors the impact of noise reduction operations on image definition and sharpness, using this feedback information to fine-tune processing parameters. This closed-loop approach ensures that noise is reduced effectively while maintaining or even enhancing image definition through adaptive parameter adjustment
Data Source
AI summary
An electronic device may include noise statistics circuitry to receive input image data corresponding to an image displayable on an electronic display. The input image data may include one or more channels of pixel data. The noise statistics circuitry may also determine a subset of pixel data of a channel of pixel data that qualifies for statistics gathering according to qualification criteria. Additionally, the noise statistics circuitry may determine noise statistics based on the subset of pixel data, and identify image features within the subset of pixel data based on the noise statistics. The image features may include frequency signatures, differentiated from noise, that correspond to features of content of the image. The electronic device may also include enhancement circuitry to enhance the input image data based on the noise statistics and the identified image features. Such enhancement circuitry may substantially preserve the image features within the input image data.