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is there any way or code on how to detect pixelated parts or distorted parts of a picture?

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Dani AI

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originally asked whether pixelated or distorted areas can be detected and was right: simple image-analysis rules usually find most pixelation before resorting to heavy models. Pixelation typically shows low internal texture together with sharp block boundaries (baseline JPEG uses 8x8 DCT blocks). Resizing/warping tends to alter local frequency content and reduce high-frequency energy.

A practical, C#-friendly pipeline (works with OpenCvSharp or Emgu CV) is:

  • convert to grayscale and optionally equalize local illumination.
  • build local-detail maps: local standard deviation or Laplacian magnitude at multiple window sizes (8, 16, 32 px).
  • threshold low-detail areas at each scale and intersect/union results. Typical starting thresholds: local stddev ~4–10 (0–255 scale) for a 16 px window (tune to data).
  • compute a blockiness score by sampling absolute differences across vertical/horizontal lines spaced every 8 px; high boundary differences combined with low internal variance are a strong pixelation cue.
  • morphologically close small holes, extract connected components and discard tiny regions (e.g., <500 px or a small percentage of image area).

To reduce false positives, combine features (stddev, entropy, DCT/FFT high-frequency energy, block-score) and train a small classifier (RandomForest/SVM) on a few dozen labeled patches. For very challenging images, create synthetic pixelated examples and train a lightweight CNN segmentation model. Recommended C# tooling: OpenCvSharp or Emgu CV for image ops and ML.NET for simple classifiers.

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Member Avatar for Rashakil Fol

Yes, there are.

Member Avatar for deathnote

how? what are the codes that ill be using to detect it? im just starting with c# as well so i only know limited stuff

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