Full-Reference Image Quality Assessment

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13 shown of 13 papers with code (41 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• PKU-I2IQA: An Image-to-Image Quality Assessment Database for AI Generated Images
  27 Nov 2023 2 repositories listed
  Although previous work has established several human perception-based AIGC image quality assessment (AIGCIQA) databases for text-generated images, the AI image generation technology includes scenarios like text-to-image…
• Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
  6 Dec 2016 2 repositories listed
  We present a deep neural network-based approach to image quality assessment (IQA).
• Toward Generalized Image Quality Assessment: Relaxing the Perfect Reference Quality Assumption
  14 Mar 2025 1 repository listed
  To relax the assumption of perfect reference image quality, we build a large-scale IQA database, namely DiffIQA, containing approximately 180, 000 images generated by a diffusion-based image enhancer with adjustable…
• Foundation Models Boost Low-Level Perceptual Similarity Metrics
  11 Sep 2024 1 repository listed
  For full-reference image quality assessment (FR-IQA) using deep-learning approaches, the perceptual similarity score between a distorted image and a reference image is typically computed as a distance measure between…
• Sliced Maximal Information Coefficient: A Training-Free Approach for Image Quality Assessment Enhancement
  19 Aug 2024 1 repository listed
  Full-reference image quality assessment (FR-IQA) models generally operate by measuring the visual differences between a degraded image and its reference.
• You Only Train Once: A Unified Framework for Both Full-Reference and No-Reference Image Quality Assessment
  14 Oct 2023 1 repository listed
  When our proposed model is independently trained on NR or FR IQA tasks, it outperforms existing models and achieves state-of-the-art performance.
• The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric
  6 Oct 2023 1 repository listed
  We show how perceptual embeddings of the visual system can be constructed at inference-time with no training data or deep neural network features.
• Saliency-Guided Local Full-Reference Image Quality Assessment
  29 Jun 2022 1 repository listed
  In addition to this, visual saliency was utilized as weights in the weighted averaging of local image quality scores, emphasizing image regions that are salient to human observers.
• SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment
  9 May 2022 1 repository listed
  In this paper, we design a full-reference image quality assessment metric SwinIQA to measure the perceptual quality of compressed images in a learned Swin distance space.
• Perceptual Image Quality Assessment with Transformers
  30 Apr 2021 1 repository listed
  In this paper, we propose an image quality transformer (IQT) that successfully applies a transformer architecture to a perceptual full-reference image quality assessment (IQA) task.
• A combined full-reference image quality assessment approach based on convolutional activation maps
  19 Oct 2020 1 repository listed
  In this study, we explore a novel, combined approach which predicts the perceptual quality of a distorted image by compiling a feature vector from convolutional activation maps.
• Quality Aware Generative Adversarial Networks
  8 Nov 2019 1 repository listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  Generative Adversarial Networks (GANs) have become a very popular tool for implicitly learning high-dimensional probability distributions.
• Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework
  1 Jul 2017 1 repository listed
  Since human observers are the ultimate receivers of digital images, image quality metrics should be designed from a human-oriented perspective.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.