Image quality

A ML model that evaluates the technical quality of images by analyzing their visual attributes


The quality of digital content is a critical factor in delivering satisfactory user experience and achieving branding goals. Our Image quality assessment (IQA) model is a machine learning solution designed to evaluate the visual quality of images.

When an image is fed into the system, the model processes it to assess various quality attributes and generates a quality score that reflects a weighted combination of those attributes, providing a precise assessment of the image's technical quality.

The attributes include multiple factors such as sharpness, blurriness, noise, dynamic range, contrast, distortion, etc. By providing a thorough image quality assessment, the model greatly enhances the management of digital assets and offers indispensable aid in content selection and optimization.

Typical use cases

Image quality assessment finds application in various domains and industries where image quality is a top priority:

  • Stock photography - Stock image providers can use the model to rate and categorize the images in their libraries, thus helping customers find ones with superior quality for their projects.

  • E-commerce - E-commerce platforms can ensure product images meet quality standards, improving the visual appeal of product listings and driving sales.

  • Publishing - Media outlets and publishing companies can automate the assessment of images for articles so their publications contain high-quality assets.

  • Advertising - Advertising agencies can use more easily evaluate the technical quality of campaign visuals, helping maintain brand consistency and professionalism.

  • Digital marketing - Marketers can optimize their social media and online content for greater impact and engagement.

API endpoints

Information about the specific API endpoints is available in an always up-to-date documentation, that can be accessed via the following link:

There, you can find detailed information about the API endpoints, together with all required request parameters, so you know how to interact with them.

Example API responses

Input imageAPI response
  "status": "success",
  "version": "3.0.2",
  "quality_score": 0.17,
  "request_uuid": "ffbba05c-6231-4d6e-a8e6-6ae3e1b9cf27",
  "sha1": "4a12c83542e39c85eb412754c9d8a8deefd2822a"
  "status": "success",
  "version": "3.0.2",
  "quality_score": 0.84,
  "request_uuid": "84ced2cf-51fc-4792-a72f-ef74910d8e48",
  "sha1": "63bf86e6595db5ec2e7422d3941c6a72f729c48d"

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