Related Work on Image Quality Assessment
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Abstract (arXiv)
Due to the existence of quality degradations introduced in various stages of visual signal acquisition, compression, transmission and display, image quality assessment (IQA) plays a vital role in image-based applications. According to whether the reference image is complete and available, image quality evaluation can be divided into three categories: Full-Reference(FR), Reduced- Reference(RR), and Non- Reference(NR). This article will review the state-of-the-art image quality assessment algorithms.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 49 references that are in the map. First posted on arXiv 11 Nov 2021.
Builds on
4 of 49 references.
References · 4
- 2018PieAPP: Perceptual Image-Error Assessment through Pairwise Preferencecited by 158
- 2019dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairscited by 73
- 2018Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learningcited by 49
- 2017A Perceptually Weighted Rank Correlation Indicator for Objective Image Quality Assessmentcited by 13
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