Image Evaluation Method for Quantifying ISP Artifacts
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Solution Overview
Problem
Image signal processors (ISPs) generate unexpected artifacts when processing high-frequency images, leading to image quality degradation.
Innovation Solution
An image evaluation method that extracts an artifact-free reference image from a distorted image with a lattice pattern, quantifies distortion by comparing the reference image with the distorted image, and optimizes ISP parameters based on the quantified result.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the ISP processes high-frequency images to maintain image detail and sharpness, then image quality is improved, but artifacts are generated causing quality degradation
Solution Approach 1:
The patent applies preliminary action by processing a reference image through the same ISP pipeline before comparing it with the test image. This pre-processed reference image serves as a baseline to identify and quantify artifacts in the test image, allowing the system to detect quality degradation before it affects final output.
Solution Approach 2:
The patent implements feedback by calculating a quality metric based on the difference between the test image and the reference image, then using this metric to determine whether to apply additional processing. The feedback loop allows the system to adapt its processing strength based on detected artifact levels, resolving the contradiction between processing intensity and artifact generation.
2Manufacturing precision
If the ISP applies strong processing to reduce noise and artifacts, then image quality is improved, but processing time increases
Solution Approach 1:
The patent uses feedback by calculating a quality metric that quantifies artifact presence, then using this metric to dynamically control processing strength. When the metric indicates low artifact levels, the system applies weaker processing to reduce time consumption. When artifacts are detected, the system strengthens processing accordingly, creating an adaptive balance between quality and time.
Solution Approach 2:
The patent applies parameter changes by adjusting processing strength based on the quality metric. The system varies parameters such as denoising intensity, sharpening strength, and artifact removal levels according to the detected conditions, allowing optimal processing time allocation rather than applying maximum processing uniformly.
3Manufacturing precision
If the system processes images to maintain sharpness and detail, then image quality is improved, but noise and artifacts are generated
Solution Approach 1:
The patent applies preliminary action by pre-processing a reference image through the complete ISP pipeline including sharpening and denoising operations. This reference image then serves as a baseline for comparison, allowing the system to identify noise and artifacts in test images without needing to re-process them through the same intensive pipeline.
Solution Approach 2:
The patent uses copying by creating a reference image that replicates the expected output quality. This reference copy is then compared with test images to detect deviations caused by noise and artifacts, avoiding the need for complex real-time analysis of each test image's quality characteristics.
Data Source
AI summary
An image evaluation method, including: obtaining a test image including a first lattice pattern formed by image edges; aligning the test image using the image edges to generate an aligned image including a second lattice pattern formed by aligned image edges; generating a compressed image by compressing the aligned image; and generating a quantified result by quantifying a per-pixel difference between the compressed image and the aligned image.