The DOI matches its Crossref record.
A generalized block-edge impairment metric for video coding
Abstract
A mew generalized block-edge impairment metric (GBIM) is presented in this paper as a quantitative distortion measure for blocking artifacts in digital video and image coding. This distortion measure does not require the original image sequence as a comparative reference, and is found to be consistent with subjective evaluation.
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Cited by
Modern Image Quality Assessment
Wang, Z., & Bovik, A. C. (2006) · Synthesis Lectures on Image, Video, and Multimedia Processing · doi:10.1007/978-3-031-02238-8 · Cited by 967
No-reference perceptual quality assessment of JPEG compressed images
Wang, Z., Sheikh, H. R., & Bovik, A. C. (2003) · Proceedings. International Conference on Image Processing · doi:10.1109/icip.2002.1038064 · Cited by 834
No-reference quality assessment using natural scene statistics: JPEG2000
Sheikh, H. R., Bovik, A. C., & Cormack, L. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.854492 · Cited by 575
Reduced-reference image quality assessment using a wavelet-domain natural image statistic model
Wang, Z., & Simoncelli, E. P. (2005) · SPIE Proceedings · doi:10.1117/12.597306 · Cited by 438
Blind measurement of blocking artifacts in images
Wang, Z., Bovik, A. C., & Evan, B. L. (2002) · Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101) · doi:10.1109/icip.2000.899622 · Cited by 413
Metadata
- Authors
- Wu, H. R., & Yuen, M.
- Venue
- IEEE Signal Processing Letters
- Published
- 1997-11
- Volume
- 4
- Issue
- 11
- Pages
- 317-320
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W2113045514
- Cited by
- 316
- References
- 0
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A generalized block-edge impairment metric for video coding. https://app.cochat.ai/papers/10.1109/97.641398
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- Wu, H. R., & Yuen, M. (1997). A generalized block-edge impairment metric for video coding. IEEE Signal Processing Letters. https://doi.org/10.1109/97.641398
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.