November 14, 2017

Introducing Landscape Quality, Portrait Quality, and Textures and Patterns Visual Recognition Models

Table of Contents:

Being in the business of computer vision, we deal a lot with photos – good and bad. But what makes a photo “good” vs. “bad” – the composition? The lighting? The way it makes you feel? We decided to try and distill those elements into an algorithm to help photographers and media managers sort through large volumes of content to find the highest quality images and video.


Being in the business of computer vision, we deal a lot with photos. These photos can range from selfies using a cell phone camera, to computer-generated images created by designers, to professional photographs using high-end DSLR cameras. Our broad range of models helps computers understand “what’s in an image” and “where the object is located in an image”. For the first time, we’re releasing models that help computers understand image quality, or “is this image good or bad?” We are happy to release Landscape Quality Model and Portrait Quality Model into Beta which understands the quality of an image, and responds back with the confidence level of whether an image is “high quality” or “low quality”.

Good quality photo attributes:

  • Good lighting
  • Sharp and in focus
  • If retouching is present, it is not obvious (no completely airbrushed skin)
  • Not too much grain/noise (**unless it’s the artist’s intention)

Poor quality photo attributes:

  • Severe chromatic aberration
  • Red eyes
  • Extremely backlit
  • Unnatural vignetting, often digitally added

“With our computer vision capabilities, we want photographers to focus on what they do best: capture amazing moments.”