This repository contains a PyTorch implementation of the deep Image Quality Assessment (deepIQA) models, as described in the paper "DeepIQA: Learning Deep Representations for Image Quality Assessment". Our implementation aims to provide an accessible and efficient way to use deep learning techniques to evaluate image quality.
Image Quality Assessment (IQA) plays a crucial role in various image processing applications, ensuring that the images meet certain quality standards. The deepIQA approach leverages deep learning models to automatically assess the quality of images, surpassing traditional methods in terms of accuracy and reliability.
- Implementation of deepIQA models in PyTorch.
- Easy-to-use interface for training and evaluating models.
- Examples of applying deepIQA to standard image quality datasets.
- Support for custom datasets and evaluation metrics.