# Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network

## TL;DR

This paper introduces a deep bilinear CNN model for blind image quality assessment that effectively handles both synthetic and authentic distortions, achieving superior performance and generalizability across multiple databases.

## Contribution

It presents a novel bilinear CNN architecture that combines specialized feature extraction for different distortion types and demonstrates state-of-the-art results in blind image quality assessment.

## Key findings

- Achieves superior performance on synthetic and authentic image quality databases.
- Demonstrates strong generalization on the Waterloo Exploration Database.
- Effectively models diverse distortions using a unified bilinear feature representation.

## Abstract

We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions. Our model consists of two convolutional neural networks (CNN), each of which specializes in one distortion scenario. For synthetic distortions, we pre-train a CNN to classify image distortion type and level, where we enjoy large-scale training data. For authentic distortions, we adopt a pre-trained CNN for image classification. The features from the two CNNs are pooled bilinearly into a unified representation for final quality prediction. We then fine-tune the entire model on target subject-rated databases using a variant of stochastic gradient descent. Extensive experiments demonstrate that the proposed model achieves superior performance on both synthetic and authentic databases. Furthermore, we verify the generalizability of our method on the Waterloo Exploration Database using the group maximum differentiation competition.