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Singular Value Decomposition (SVD) Image Coding
Abstract
The numerical techniques of transform image coding are well known in the image bandwidth compression literature. This concise paper presents a new transform method in which the singular values and singular vectors of an image are computed and transmitted instead of transform coefficients. The singular value decomposition (SVD) method is known to be the deterministically optimal transform for energy compaction [2]. A systems implementation is hypothesized, and a variety of coding strategies is developed. Statistical properties of the SVD are discussed and a self adaptive set of experimental results is presented, Imagery compressed to 1, 1.5, and 2.5 bits per pixel with less than 1.6, 1, and 1/3 percent, respective mean-square error is displayed. Finally, additional image coding scenarios are postulated for further consideration.
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Cited by
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Jain, A. K. (1981) · Proceedings of the IEEE · doi:10.1109/proc.1981.11971 · Cited by 709
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Systematic Survey on Visually Meaningful Image Encryption Techniques
Himthani, V., Dhaka, V. S., Kaur, M., Singh, D., & Lee, H.-N. (2022) · IEEE Access · doi:10.1109/access.2022.3203173 · Cited by 20
Amortized Neural Networks for Low-Latency Speech Recognition
Macoskey, J., Strimel, G. P., Su, J., & Rastrow, A. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-712 · Cited by 12
Metadata
- Authors
- Andrews, H., & Patterson, C.
- Venue
- IEEE Transactions on Communications
- Published
- 1976-04
- Volume
- 24
- Issue
- 4
- Pages
- 425-432
- ISSN
- 0096-2244 (ISSN-L)
- OpenAlex
- W1987570579
- Cited by
- 304
- References
- 0
- FWCI
- 1.40
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Singular Value Decomposition (SVD) Image Coding. https://app.cochat.ai/papers/10.1109/tcom.1976.1093309
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- Andrews, H., & Patterson, C. (1976). Singular Value Decomposition (SVD) Image Coding. IEEE Transactions on Communications. https://doi.org/10.1109/tcom.1976.1093309
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