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Image Quality Detection Analysis Tool

IT Ops & Security Updated 2026.08.29

Paste the following prompt into your AI chat to install this skill:

Please install @user_bb47e3e3/image-quality-detection-analysis according to https://skillhub.cn/install/skillhub.md.

About this skill

Problem

Camera feeds can quietly develop black frames, overexposure, color casts, stripe interference, snow noise, or blur. These issues may not trigger alerts, and manual frame-by-frame inspection is slow. This skill evaluates captured images or video frames from cameras, helping operators distinguish equipment faults from environmental interference. It is useful for daily self-checks of security monitoring systems and routine camera maintenance inspections.

Capabilities and workflow

  • Checks: blackout, overexposure, color cast, stripes, snow noise, blurriness, and sharpness scoring.
  • Input: local jpg/jpeg/png/mp4/avi/mov files or URLs, up to 10MB.
  • Method: invokes scripts.image_quality_detection_analysis and uses brightness histograms, spectral analysis, and color distribution modeling to identify issue types.
  • Output: basic image info, quality score, issue categories, severity, and calibration suggestions. History listings are fetched from the cloud API and rendered as a Markdown table.

Boundaries

A valid open-id is required before analysis; values should not be assumed. History queries should use the cloud API rather than local memory. The report is a maintenance aid and does not replace professional hardware inspection.

Use Cases

  • A security ops engineer sees a washed-out camera feed and needs to distinguish color cast from lens contamination.
  • A monitoring team receives reports of snowy video and needs per-frame noise and sharpness scoring.
  • A field technician uploads camera stills and needs a quality report for later comparison.
  • An on-call engineer reviews historical reports to confirm whether a black screen is an equipment fault or environmental issue.

Best For

  • Security camera maintenance engineers who need to identify blackouts, color casts, or blur quickly.
  • Monitoring center operators who need camera anomaly frames converted into retainable quality reports.
  • Video system integrators who need to verify camera image quality before handoff.
  • After-sales support engineers who need to troubleshoot reported snow noise, stripes, or poor sharpness.