The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Perceptual visual quality metrics: A survey
Abstract
Visual quality evaluation has numerous uses in practice, and also plays a central role in shaping many visual processing algorithms and systems, as well as their implementation, optimization and testing. In this paper, we give a systematic, comprehensive and up-to-date review of perceptual visual quality metrics (PVQMs) to predict picture quality according to human perception. Several frequently used computational modules (building blocks of PVQMs) are discussed. These include signal decomposition, just-noticeable distortion, visual attention, and common feature and artifact detection. Afterwards, different types of existing PVQMs are presented, and further discussion is given toward feature pooling, viewing condition, computer-generated signal and visual attention. Six often-used image metrics(namely SSIM, VSNR, IFC, VIF, MSVD and PSNR) are also compared with seven public image databases (totally 3832 test images). We highlight the most significant research work for each topic and provide the links to the extensive relevant literature.
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Cites
Image quality assessment: from error visibility to structural similarity
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Neural Mechanisms of Selective Visual Attention
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“As the simplest model-based approach, the HVS is regarded as a single spatial filter characterized by the spatial CSF, and such early models have been developed for images [43,97] and video [94,151].”
BackgroundMethodA no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB)
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Cited by
Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
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Image database TID2013: Peculiarities, results and perspectives
Ponomarenko, N., Jin, L., Ieremeiev, O., Lukin, V., Egiazarian, K., Astola, J., Vozel, B., Chehdi, K., Carli, M., Battisti, F., et al. (2015) · Signal Processing: Image Communication · doi:10.1016/j.image.2014.10.009 · Cited by 1,241
A Survey on Quality of Experience of HTTP Adaptive Streaming
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Mean opinion score (MOS) revisited: Methods and applications, limitations and alternatives
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PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference
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Metadata
- Authors
- Lin, W., & Jay Kuo, C.-C.
- Venue
- Journal of Visual Communication and Image Representation
- Publisher
- Elsevier BV
- Published
- 2011-05
- Volume
- 22
- Issue
- 4
- Pages
- 297-312
- ISSN
- 1047-3203 (ISSN-L)
- OpenAlex
- W1973175507
- Cited by
- 922
- References
- 16
- FWCI
- 73.15
- Type
- journal article
- Language
- en
- Open access
- closed
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