# Test Time Adaptation for Blind Image Quality Assessment

arXiv: https://arxiv.org/abs/2307.14735 · HTML: https://mlsanity.com/paper/2307.14735/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 47 references that are in the map.
- First posted on arXiv 27 Jul 2023.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.08546/) (2020)
- [Improving robustness against common corruptions by covariate shift adaptation](https://mlsanity.com/paper/2006.16971/) (2020)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift](https://mlsanity.com/paper/2006.10963/) (2020)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality](https://mlsanity.com/paper/1912.10088/) (2019)
- [Test-Time Training with Masked Autoencoders](https://mlsanity.com/paper/2209.07522/) (2022)
- [Massive Online Crowdsourced Study of Subjective and Objective Picture Quality](https://mlsanity.com/paper/1511.02919/) (2015)
- [PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration](https://mlsanity.com/paper/2007.12142/) (2020)
- [Colorization as a Proxy Task for Visual Understanding](https://mlsanity.com/paper/1703.04044/) (2017)
- [No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency](https://mlsanity.com/paper/2108.06858/) (2021)
- [RankIQA: Learning from Rankings for No-reference Image Quality Assessment](https://mlsanity.com/paper/1707.08347/) (2017)
- [MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2004.05508/) (2020)
- [Unsupervised Learning by Predicting Noise](https://mlsanity.com/paper/1704.05310/) (2017)
- [Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation](https://mlsanity.com/paper/2106.14999/) (2021)
- [Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature Alignment](https://mlsanity.com/paper/2206.13951/) (2022)
- [Quality Assessment of Low Light Restored Images: A Subjective Study and an Unsupervised Model](https://mlsanity.com/paper/2202.02277/) (2022)

## Built on by (most cited first, 11 of 11)

- [Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare](https://mlsanity.com/paper/2405.19298/) (2024)
- [Quality-Aware Image-Text Alignment for Opinion-Unaware Image Quality Assessment](https://mlsanity.com/paper/2403.11176/) (2024)
- [VisualCritic: Making LMMs Perceive Visual Quality Like Humans](https://mlsanity.com/paper/2403.12806/) (2024)
- [Video Quality Assessment Based on Swin TransformerV2 and Coarse to Fine Strategy](https://mlsanity.com/paper/2401.08522/) (2024)
- [Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updates](https://mlsanity.com/paper/2504.02008/) (2025)
- [Image and Video Quality Assessment using Prompt-Guided Latent Diffusion Models for Cross-Dataset Generalization](https://mlsanity.com/paper/2406.04654/) (2024)
- [Test-time adaptation for image compression with distribution regularization](https://mlsanity.com/paper/2410.12191/) (2024)
- [Attention Down-Sampling Transformer, Relative Ranking and Self-Consistency for Blind Image Quality Assessment](https://mlsanity.com/paper/2409.07115/) (2024)
- [ELIQ: A Label-Free Framework for Quality Assessment of Evolving AI-Generated Images](https://mlsanity.com/paper/2602.03558/) (2026)
- [A Lightweight Parallel Framework for Blind Image Quality Assessment](https://mlsanity.com/paper/2402.12043/) (2024)
- [PIGUIQA: A Physical Imaging Guided Perceptual Framework for Underwater Image Quality Assessment](https://mlsanity.com/paper/2412.15527/) (2024)

## Abstract (arXiv)

While the design of blind image quality assessment (IQA) algorithms has improved significantly, the distribution shift between the training and testing scenarios often leads to a poor performance of these methods at inference time. This motivates the study of test time adaptation (TTA) techniques to improve their performance at inference time. Existing auxiliary tasks and loss functions used for TTA may not be relevant for quality-aware adaptation of the pre-trained model. In this work, we introduce two novel quality-relevant auxiliary tasks at the batch and sample levels to enable TTA for blind IQA. In particular, we introduce a group contrastive loss at the batch level and a relative rank loss at the sample level to make the model quality aware and adapt to the target data. Our experiments reveal that even using a small batch of images from the test distribution helps achieve significant improvement in performance by updating the batch normalization statistics of the source model.