Image inpainting concept in Computer Vision - Model Metrics & Evaluation

Metrics & Evaluation - Image inpainting concept

Which metric matters for Image Inpainting and WHY

Image inpainting means filling missing parts of an image so it looks natural. To check how well the model fills these gaps, we use metrics that compare the filled image to the original complete image.

Common metrics include:

• PSNR (Peak Signal-to-Noise Ratio): Measures how close the filled image is to the original. Higher PSNR means better quality.
• SSIM (Structural Similarity Index): Checks if the structure and textures look similar. Values closer to 1 mean the images are very alike.
• L1 or L2 Loss: Measures pixel differences. Lower values mean the inpainted area is closer to the original.

These metrics matter because they tell us if the model is realistically restoring missing parts, not just guessing random pixels.

Confusion Matrix or Equivalent Visualization

Image inpainting is not a classification task, so it does not use a confusion matrix. Instead, we visualize results by comparing images side-by-side:

Original Image       Masked Image (with holes)       Inpainted Image
[Complete photo]     [Photo with missing parts]      [Model fills missing parts]

We also use difference images showing pixel errors:
Difference = |Original - Inpainted|
Lower difference means better inpainting.
    

Precision vs Recall Tradeoff (Equivalent for Image Inpainting)

In image inpainting, the tradeoff is between:

• Detail preservation: Keeping fine textures and edges sharp.
• Smoothness and consistency: Avoiding unnatural artifacts or noise.

If the model focuses too much on smoothness, it may blur details (low detail precision). If it tries to keep all details, it may create strange artifacts (low consistency recall).

Good inpainting balances these, producing images that look natural and detailed.

What "Good" vs "Bad" Metric Values Look Like

• PSNR: Good values are usually above 30 dB, meaning the inpainted image is very close to original. Below 20 dB is poor quality.
• SSIM: Values close to 1 (like 0.9 or above) show strong similarity. Values below 0.7 indicate poor structural match.
• L1/L2 Loss: Lower is better. Good models have small pixel errors, bad models have large differences.

Visually, good inpainting looks seamless and natural. Bad inpainting shows blurry, patchy, or obviously fake areas.

Common Metrics Pitfalls

• Relying only on PSNR or SSIM: These metrics may not capture perceptual quality well. An image can have good PSNR but look unnatural.
• Ignoring visual inspection: Human eyes are good at spotting fake areas, so always check images visually.
• Overfitting: Model may memorize training images and produce good metrics but fail on new images.
• Data leakage: Using test images in training can give falsely high metrics.

Self-Check Question

Your image inpainting model shows a PSNR of 35 dB but the filled areas look blurry and unnatural. Is this model good?

Answer: Not necessarily. High PSNR means pixel values are close, but blurriness shows the model may be averaging pixels and losing details. You should also check SSIM and visual quality to confirm.

Key Result

PSNR and SSIM are key metrics to measure how well the inpainted image matches the original in quality and structure.