The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
A Haar wavelet-based perceptual similarity index for image quality assessment
Open accessAbstract
Abstract In most practical situations, the compression or transmission of images and videos creates distortions that will eventually be perceived by a human observer. Vice versa, image and video restoration techniques, such as inpainting or denoising, aim to enhance the quality of experience of human viewers. Correctly assessing the similarity between an image and an undistorted reference image as subjectively experienced by a human viewer can thus lead to significant improvements in any transmission, compression, or restoration system. This paper introduces the Haar wavelet-based perceptual similarity index (HaarPSI), a novel and computationally inexpensive similarity measure for full reference image quality assessment. The HaarPSI utilizes the coefficients obtained from a Haar wavelet decomposition to assess local similarities between two images, as well as the relative importance of image areas. The consistency of the HaarPSI with the human quality of experience was validated on four large benchmark databases containing thousands of differently distorted images. On these databases, the HaarPSI achieves higher correlations with human opinion scores than state-of-the-art full reference similarity measures like the structural similarity index (SSIM), the feature similarity index (FSIM), and the visual saliency-based index (VSI). Along with the simple computational structure and the short execution time, these experimental results suggest a high applicability of the HaarPSI in real world tasks.
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Cites
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Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2004) · The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003 · doi:10.1109/acssc.2003.1292216 · Cited by 6,253
FSIM: A Feature Similarity Index for Image Quality Assessment
Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2109730 · Cited by 5,203
Image information and visual quality
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Cited by
From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
Ying, Z., Niu, H., Gupta, P., Mahajan, D., Ghadiyaram, D., & Bovik, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00363 · Cited by 406
PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference
Prashnani, E., Cai, H., Mostofi, Y., & Sen, P. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00194 · Cited by 339
Assessing Image Quality Issues for Real-World Problems
Chiu, T.-Y., Zhao, Y., & Gurari, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00370 · Cited by 63
Robust Vision-Based Cheat Detection in Competitive Gaming
Jonnalagadda, A., Frosio, I., Schneider, S., McGuire, M., & Kim, J. (2021) · Proceedings of the ACM on Computer Graphics and Interactive Techniques · doi:10.1145/3451259 · Cited by 23
A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps
Varga, D. (2020) · Algorithms · doi:10.3390/a13120313 · Cited by 11
Metadata
- Authors
- Reisenhofer, R., Bosse, S., Kutyniok, G., & Wiegand, T.
- Venue
- Signal Processing: Image Communication
- Publisher
- Elsevier BV
- Published
- 2018-02
- Volume
- 61
- Pages
- 33-43
- ISSN
- 0923-5965 (ISSN-L)
- OpenAlex
- W2481798478
- Cited by
- 312
- References
- 37
- FWCI
- 11.46
- Type
- journal article
- Language
- en
- Open access
- green
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