Official implementation for "Image Quality Assessment using Contrastive Learning"
A Python port of the MATLAB reference implementation
A Multi-channel CNN for Blind 360-Degree Image Quality Assessment
Analysis of video quality datasets via design of minimalistic video quality models
Official implementation for "CONVIQT: Contrastive Video Quality Estimator"
Non-local Modeling for Image Quality Assessment
Automated blind MRI quality assessment using 3D CNN + FC deep learning. Trains on ABIDE-1 (15 sites), achieves SOTA transfer to novel sites and Glioblastoma MRI from TCIA.
🏆 🥇 Winner Solution for the FR Track and Second Solution for the NR Track of ICME 2025 Generalizable HDR and SDR Video Quality Measurement Grand Challenge
Official PyTorch implementation of "R3-PCQA: Ray-Reprojection-Reinforcement for No-Reference 3D Point Cloud Quality Assessment" (CVPR 2026).
Source code for the paper "Blind Image Quality Assessment of Authentically Distorted Images"
This repository contains the source code to reproduce the paper "Feature-based No-Reference Video Quality Assessment using Extra Trees".
A fast, no-reference video quality benchmarking tool using BRISQUE and other IQA metrics. Extracts sampled frames, computes perceptual quality scores, and compares encodes objectively.
No-reference Video Quality Assessment
Pure Go BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator) — no-reference image quality scoring with zero dependencies, zero allocations, and OpenCV-compatible results.
To associate your repository with the no-reference topic, visit your repo's landing page and select "manage topics."