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Visual Features Metric

The Visual Features metric extracts standard image quality indicators from image datasets. It is used to analyze characteristics like brightness, contrast, and sharpness.

What It Measures

Visual Features extracts image quality metrics to assess your image dataset. Use it to:

  • Check image quality — Identify blurry or dark images
  • Validate dataset health — Ensure images meet quality thresholds
  • Preprocessing validation — Check augmentation results

Available Features

Feature What It Measures Use Case
Luminosity Brightness level Detect under/over-exposed images
Contrast RMS contrast Find low-contrast images
Blur Laplacian variance Identify blurry images
Entropy Shannon entropy Measure information content

Use Cases

  • Filter low-quality images from training data
  • Validate image preprocessing pipeline
  • Monitor image quality in production
  • Check dataset balance across brightness levels

Processor Information

  • Class: VisualFeaturesProcessor
  • Package: dqm-ml-images
  • Type Name: visual_metric

Computed Features

  • Luminosity: Mean gray level of the image.
  • Contrast: Root Mean Square (RMS) contrast.
  • Blur: Variance of the Laplacian, used to estimate the level of focus/sharpness.
  • Entropy: Shannon entropy of the image histogram.

Configuration Parameters

  • input_columns: Column name containing image bytes or local file paths.
  • grayscale: Boolean, whether to convert images to grayscale before processing (default: true).

Example YAML Configuration

metrics_processor:
  image_quality:
    type: visual_metric
    input_columns: ["image_data"]
    grayscale: true

Output

The processor generates the following feature columns in the output:

  • m_luminosity
  • m_contrast
  • m_blur_level
  • m_entropy