NIQE (Natural Image Quality Evaluator, IEEE Signal Processing Letters 2013) is a classic handcrafted no-reference image-quality model that shares BRISQUE's natural scene statistics foundation but is completely opinion-unaware, meaning training-free: it needs no human-labeled quality scores at all. Instead of learning from rated distortions, it fits a statistical model to a corpus of pristine natural photographs and then scores how far a test image deviates from that pristine model, so a larger deviation implies lower quality. This makes it attractive for deployment anywhere, since it requires no labeled data, and it is available through toolboxes such as pyiqa and scikit-video. It scores a single frame, so for video it is run per frame and pooled. Its catch is that being training-free and tuned to photographic regularities, it is among the least accurate blind metrics on messy real-world content, with reported cross-dataset SROCC around 0.18 to 0.46, so its output must be banded and validated on footage like yours.
