# Subjective and Objective Quality Assessment of Image: A Survey

arXiv: https://arxiv.org/abs/1406.7799 · HTML: https://mlsanity.com/paper/1406.7799/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2014.

## Built on by (most cited first, 14 of 14)

- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [A survey on IQA](https://mlsanity.com/paper/2109.00347/) (2021)
- [X-Fake: Juggling Utility Evaluation and Explanation of Simulated SAR Images](https://mlsanity.com/paper/2407.19436/) (2024)
- [A Multi-task convolutional neural network for blind stereoscopic image quality assessment using naturalness analysis](https://mlsanity.com/paper/2106.09303/) (2021)
- [Image Quality Assessment for Magnetic Resonance Imaging](https://mlsanity.com/paper/2203.07809/) (2022)
- [Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality](https://mlsanity.com/paper/2505.19696/) (2025)
- [Weighted Fuzzy-Based PSNR for Watermarking](https://mlsanity.com/paper/2101.08502/) (2021)
- [Assessing Image Quality Using a Simple Generative Representation](https://mlsanity.com/paper/2404.18178/) (2024)
- [Evaluating Deep Neural Networks for Image Document Enhancement](https://mlsanity.com/paper/2106.15286/) (2021)
- [Learning with Noisy Low-Cost MOS for Image Quality Assessment via Dual-Bias Calibration](https://mlsanity.com/paper/2311.15846/) (2023)
- [Swin Transformer for Robust Differentiation of Real and Synthetic Images: Intra- and Inter-Dataset Analysis](https://mlsanity.com/paper/2409.04734/) (2024)
- [Point Cloud Quality Assessment Using the Perceptual Clustering Weighted Graph (PCW-Graph) and Attention Fusion Network](https://mlsanity.com/paper/2506.04081/) (2025)
- [Component-Aware Sketch-to-Image Generation Using Self-Attention Encoding and Coordinate-Preserving Fusion](https://mlsanity.com/paper/2603.09484/) (2026)
- [SHAMISA: SHAped Modeling of Implicit Structural Associations for Self-supervised No-Reference Image Quality Assessment](https://mlsanity.com/paper/2603.13669/) (2026)

## Abstract (arXiv)

With the increasing demand for image-based applications, the efficient and reliable evaluation of image quality has increased in importance. Measuring the image quality is of fundamental importance for numerous image processing applications, where the goal of image quality assessment (IQA) methods is to automatically evaluate the quality of images in agreement with human quality judgments. Numerous IQA methods have been proposed over the past years to fulfill this goal. In this paper, a survey of the quality assessment methods for conventional image signals, as well as the newly emerged ones, which includes the high dynamic range (HDR) and 3-D images, is presented. A comprehensive explanation of the subjective and objective IQA and their classification is provided. Six widely used subjective quality datasets, and performance measures are reviewed. Emphasis is given to the full-reference image quality assessment (FR-IQA) methods, and 9 often-used quality measures (including mean squared error (MSE), structural similarity index (SSIM), multi-scale structural similarity index (MS-SSIM), visual information fidelity (VIF), most apparent distortion (MAD), feature similarity measure (FSIM), feature similarity measure for color images (FSIMC), dynamic range independent measure (DRIM), and tone-mapped images quality index (TMQI)) are carefully described, and their performance and computation time on four subjective quality datasets are evaluated. Furthermore, a brief introduction to 3-D IQA is provided and the issues related to this area of research are reviewed.