Image Sorting and Composition Assessment Part 2 - Image Quality Assessment
Approaching photo quality assessment from Machine Learning point of view
Image Quality Assessment overview
In the previous post, I have examined the continuous learning aspects of the culling pipeline. But the most important logic of the pipeline is the Image Quality Assessment (IQA) portion of the pipeline, which quantifies the actual quality of the images, based on characteristics like noise level, closed eyes, lighting, and various other composition metrics.
There are many things to consider when we want to formulate IQA in the image culling settings. Do we want to be able to measure noise level, all the metrics seperately, or single unified quality score? Do we need to create ensemble models? How are we going to generate the test datasets? But before any of above complex ideas are addressed, it is important to see how IQA is done generally.
Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild
This is the paper that I came acorss at the CVPR 2023 conference.
