Hey everyone 👋 I just released BRISQUE v0.1.0 , a Python package for no-reference image quality assessment (NR-IQA) . If you’re working with real-world image pipelines, you often don’t have a pristine reference image to compare against — but you still need a quantitative quality signal. That’s where BRISQUE comes in. 🚀 What’s New in v0.1.0 ✅ Train Custom Models New BRISQUETrainer class lets you train models on your own datasets. Useful if you’re dealing with: Medical images Satellite imagery Underwater images Custom distortions Domain-specific data ✅ Flexible Dataset Loading Load individual images Or load from CSV with quality scores Supports MOS, DMOS, and custom scales ✅ Built-in Evaluation Metrics Includes: RMSE PLCC SROCC So you can properly benchmark your trained model. ✅ Custom Model Integration Trained models can be directly used with the BRISQUE class. ✅ Better Image Support Now handles: RGBA Grayscale Float images ✅ SciPy 1.8+ Compatibility Fix Resolved issues with newer SciPy versions. 📦 Install pip install brisque 🧠 Minimal Example from brisque import BRISQUE obj = BRISQUE(url=False) score = obj.score(image_array) # Lower = better quality 🔗 Links Code: GitHub https://github.com/rehanguha/brisque Package: PyPI https://pypi.org/project/brisque/ DOI (archived release): Zenodo 10.5281/zenodo.11104461 Would appreciate feedback, issues, PRs, or feature requests. If you’re using IQA in production or research, I’d be especially interested in: Performance on large-scale pipelines Domain-specific datasets Comparison vs deep NR-IQA approaches Thanks for checking it out 🙏 submitted by /u/rehanguha
Originally posted by u/rehanguha on r/ArtificialInteligence