Simplifying Image Enhancement with Auto Parameter
Almost every image can benefit from a little touch-up, whether it’s color correction, removing red eyes from people, or adjusting the exposure. The imgix URL API offers enhancement parameters to handle exposure (exp), gamma correction (gam), and saturation (sat) for just these cases, losslessly adjusting the output appearance of your images.
However, most aesthetic image adjustments will not be suitable across entire image libraries. So we decided to simplify the image adjustment process by creating our own special recipe for making image sets more vibrant and compelling, while minimizing your effort: the auto parameter.
When set to true, auto adjusts an image by using the distribution of highlights, midtones, and shadows across all three color channels: red, green, and blue (RGB).
yourexample.imgix.net/image.jpg?auto=true
auto=true
auto=false
The following side-by-side comparisons show the original image and the auto enhanced version:
Depending on the image, the results may range from subtle to striking. Each enhance operation takes into account the current image and uses it as a base to create a more vibrant, engaging, and better-looking image. The bonus is that you don’t need to reprocess your images—auto can be switched on for millions of images at once, saving you valuable time.
Red-eye Removal & Advanced Options
The auto enhance feature also provides red-eye removal. Adding fit=crop and crop=faces to the URL string will enable face detection, and auto enhance will look for and attempt to resolve any red-eye issues that are detected.
In the event you want to further fine-tune the application of auto enhance or the red-eye removal parameter—such as by separating the red-eye removal from the other features of auto enhance and employing red-eye removal on its own—you can use the following values for auto instead:
auto=enhance,redeye
auto=enhance
auto=redeye
This formatting will also allow for adding future auto enhancement operations.
The image comparison below demonstrates the auto-enhanced image, and then the same image (unenhanced) but treated with red-eye removal.
