This API predicts the BRISQUE score by using a support vector regression (SVR) model trained on an image database with corresponding differential mean opinion score (DMOS) values. The database contains images with known distortion such as compression artifacts, blurring, and noise, and it contains pristine versions of the distorted images. The image to be scored must have at least one of the distortions for which the model was trained.
The BRISQUE score typically has a value between 0 and 100, with 0 representing the best quality, and 100 the worst
This API is especially useful in the following use cases:
- It will be useful as an input on product sorting for e-commerce sites
- It is useful for filtering out the products with low image quality score from advertising. So you won't spend money on ads with low CTRs
- It is useful to keep your classified business clean with running this API when users upload a new photo.
...and many more use cases you can imagine.
It works both by uploading an image file programmatically and by passing an URL. It will automatically fetch the file and run the algorithm.
- Mittal, A., A. K. Moorthy, and A. C. Bovik. "No-Reference Image Quality Assessment in the Spatial Domain." IEEE Transactions on Image Processing. Vol. 21, Number 12, December 2012, pp. 4695–4708.
- Mittal, A., A. K. Moorthy, and A. C. Bovik. "Referenceless Image Spatial Quality Evaluation Engine." Presentation at the 45th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, November 2011.