The Laplacian pyramid as a compact image code
Abstract
We describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions. The representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space. Pixel-to-pixel correlations are first removed by subtracting a lowpass filtered copy of the image from the image itself. The result is a net data compression since the difference, or error, image has low variance and entropy, and the low-pass filtered image may represented at reduced sample density. Further data compression is achieved by quantizing the difference image. These steps are then repeated to compress the low-pass image. Iteration of the process at appropriately expanded scales generates a pyramid data structure. The encoding process is equivalent to sampling the image with Laplacian operators of many scales. Thus, the code tends to enhance salient image features. A further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression. Fast algorithms are described for coding and decoding.
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Use of Gaussian pyramid decomposition in image hashing as proposed in this paper is effective.
“Although pyramid decomposition has been widely used in image compression [8], computer vision [9], etc., its use in image hashing, as proposed in this paper, has proved to be every effective.”
From A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids (2019) · cites this paper as [8]
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“Although pyramid decomposition has been widely used in image compression [8], computer vision [9], etc.”
Method
- Authors
- Burt, P., & Adelson, E.
- Affiliations
- Rensselaer Polytechnic Institute (US)
- RCA (United States) (US)
- Venue
- IEEE Transactions on Communications
- Published
- 1983-04
- Volume
- 31
- Issue
- 4
- Pages
- 532-540
- ISSN
- 0096-2244 (ISSN-L)
- OpenAlex
- W2103504761
- Cited by
- 6,077
- References
- 13
- Citations by year
- 2026: 113 · 2025: 100 · 2024: 115 · 2023: 136 · 2022: 173 · 2021: 217 · 2020: 267 · 2019: 231 · 2018: 243 · 2017: 232 · 2016: 233 · 2015: 242 · 2014: 250 · 2013: 233 · 2012: 241
- FWCI
- 21.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IRE Transactions on Communications Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Data Compression Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Signal Processing › Video Coding and Compression Technologies
- Keywords
- Laplacian pyramid, image compression, multi-scale representation, low-pass filtering, quantization
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