Cloudimage - Documentation

Optipress - Machine Learning based image compression

The main challenge with JPEG compression is to find the most optimal compression factor for each different image. Different images compressed with the same compression parameters may result in different perceived quality. Moreover, the same image in different sizes may require different compression strategies to achieve maximum size gain without visible quality loss.

To overcome this, we have developed the Optipress JPEG compression algorithm.

  • Optipress finds out the best JPG compression approach by analysing specific image features and current compression parameters.
  • A Machine Learning model determines the best compression strategy for this image.
  • Quality is then evaluated based on a simulation model of the Human Visual System to achieve optimum compression without perceptive quality deterioration.


optipress=1most conservative setting. Image quality is prioritised
optipress=2balanced setting
optipress=3most aggressive setting. Optimised for smaller file size

Images created by Optipress fully comply with baseline JPEG specifications and are compatible with all JPEG encoders.

By using Optipress, you can get the most of the JPEG compression format


filter valuefile size
q=90623 KB
q=85531 KB
optipress=2513 KB
optipress=3482 KB

In this example, Optipress increases compression to achieve better compression rate, compared to standard JPEG compression approach.

filter valuefile size
q=90346 KB
q=85231 KB
optipress=2418 KB
optipress=3342 KB

With this image, Optipress detects faster perceptive quality degradation and compresses with more conservative approach.