Discrete Cosine Transform
Abstract
A discrete cosine transform (DCT) is defined and an algorithm to compute it using the fast Fourier transform is developed. It is shown that the discrete cosine transform can be used in the area of digital processing for the purposes of pattern recognition and Wiener filtering. Its performance is compared with that of a class of orthogonal transforms and is found to compare closely to that of the Karhunen-Loève transform, which is known to be optimal. The performances of the Karhunen-Loève and discrete cosine transforms are also found to compare closely with respect to the rate-distortion criterion.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
DCT concentrates image energy in the low-frequency region, which is suitable for extracting the main features of the image.
“DCT concentrates the image energy in the low-frequency region, which is suitable for extracting the main features of the image [51].”
From Digital Image Copyright Protection and Management Approach—Based on Artificial Intelligence and Blockchain Technology (2025) · cites this paper as [51]
Cites
Generalized Wiener Filtering Computation Techniques
Pratt, W. K. (1972) · IEEE Transactions on Computers · doi:10.1109/t-c.1972.223567 · Cited by 235 · see citing works
Pratt, W. K., Chen, W.-H., & Welch, L. R. (1974) · IEEE Transactions on Communications · doi:10.1109/tcom.1974.1092335 · Cited by 217 · see citing works
Computer Evaluation of Mathematical Functions
Fike, C. T. (1968) · CERN Document Server (European Organization for Nuclear Research) · Cited by 108 · see citing works
Spatial Transform Coding of Color Images
Pratt, W. K. (1971) · IEEE Transactions on Communication Technology · doi:10.1109/tcom.1971.1090769 · Cited by 106 · see citing works
Multidimensional Rotations in Feature Selection
Andrews, H. C. (1971) · IEEE Transactions on Computers · doi:10.1109/t-c.1971.223400 · Cited by 97 · see citing works
Performance Measures for Transform Data Coding
Pearl, J., Andrews, H., & Pratt, W. K. (1972) · IRE Transactions on Communications Systems · doi:10.1109/tcom.1972.1091168 · Cited by 57 · see citing works
Waish Processing of Random Signals
Pearl, J. (1971) · IEEE Transactions on Electromagnetic Compatibility · doi:10.1109/temc.1971.303143 · Cited by 19 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Fast Computational Algorithm for the Discrete Cosine Transform
Chen, W.-H., Smith, C., & Fralick, S. (1977) · IEEE Transactions on Communications · doi:10.1109/tcom.1977.1093941 · Cited by 1,148 · see citing works
Thinking in Frequency: Face Forgery Detection by Mining Frequency-Aware Clues
Qian, Y., Yin, G., Sheng, L., Chen, Z., & Shao, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58610-2_6 · Cited by 1,058 · see citing works
Fast Algorithms for Signal Processing
Blahut, R. E. (2010) · doi:10.1017/cbo9780511760921 · Cited by 274 · see citing works
Continuous Pain Intensity Estimation from Facial Expressions
Kaltwang, S., Rudovic, O., & Pantic, M. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33191-6_36 · Cited by 245 · see citing works
AFEW-VA database for valence and arousal estimation in-the-wild
Kossaifi, J., Tzimiropoulos, G., Todorovic, S., & Pantic, M. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.02.001 · Cited by 236 · see citing works
Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges
Patel, K., Mehta, D., Mistry, C., Gupta, R., Tanwar, S., Kumar, N., & Alazab, M. (2020) · IEEE Access · doi:10.1109/access.2020.2993803 · Cited by 157 · see citing works
The face deepfake detection challenge
Guarnera, L., Giudice, O., Guarnera, F., Ortis, A., Puglisi, G., Paratore, A., Bui, L. M. Q., Fontani, M., Coccomini, D. A., Caldelli, R., et al. (2022) · Journal of Imaging · doi:10.3390/jimaging8100263 · Cited by 79 · see citing works
A comparison between audio and IMU data to detect chewing events based on an earable device
Lotfi, R., Tzanetakis, G., Eskicioglu, R., & Irani, P. (2020) · Proceedings of the 11th Augmented Human International Conference · doi:10.1145/3396339.3396362 · Cited by 24 · see citing works
Effective near-duplicate image detection using perceptual hashing and deep learning
Jakhar, Y., & Borah, M. D. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104086 · Cited by 17 · see citing works
Xu, J., Zhang, J., & Wang, J. (2025) · Journal of Theoretical and Applied Electronic Commerce Research · doi:10.3390/jtaer20020076 · Cited by 9 · see citing works
“…matrix I to the frequency domain matrix F after DCT, as shown in Equation (6), where N is the image size and F ( u, v ) indicates the frequency domain coefficients, DCT concentrates the image energy in the low-frequency region, which is suitable for extracting the main features of the image [51].”
Digital signal processing in studies of animal acoustical communication, including human speech
Davis, R. O. (1986) · Computer Methods and Programs in Biomedicine · doi:10.1016/0169-2607(86)90050-7 · Cited by 6 · see citing works
Jain, S., Cretu, A.-M., & de Montjoye, Y.-A. (2021) · arXiv · doi:10.48550/arxiv.2106.09820 · Cited by 21 · see citing works
Xu, H., Wang, Y., Wang, Z., Ba, Z., Liu, W., Jin, L., Weng, H., Wei, T., & Ren, K. (2024) · Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3658644.3690238 · Cited by 2 · see citing works
FAST: Efficient Action Tokenization for Vision-Language-Action Models
Pertsch, K., Stachowicz, K., Ichter, B., Driess, D., Nair, S., Vuong, Q., Mees, O., Finn, C., & Levine, S. (2025) · arXiv · doi:10.48550/arxiv.2501.09747 · Cited by 73 · see citing works
- Authors
- Ahmed, N., Natarajan, T., & Rao, K. R.
- Affiliations
- Kansas State University (US)
- The University of Texas at Arlington (US)
- Venue
- IEEE Transactions on Computers
- Published
- 1974-01
- Volume
- C-23
- Issue
- 1
- Pages
- 90-93
- ISSN
- 0018-9340 (ISSN-L)
- 1557-9956
- 2326-3814
- OpenAlex
- W2031614119
- Cited by
- 5,156
- References
- 7
- Citations by year
- 2026: 221 · 2025: 227 · 2024: 267 · 2023: 236 · 2022: 248 · 2021: 278 · 2020: 248 · 2019: 182 · 2018: 191 · 2017: 172 · 2016: 149 · 2015: 210 · 2014: 195 · 2013: 187 · 2012: 181
- FWCI
- 21.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Computers (publishedVersion)
- Funders
- University of Southern California
- Topics
- Physical Sciences › Computer Science › Signal Processing › Digital Filter Design and Implementation
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Data Compression Techniques
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical Analysis and Transform Methods
- Keywords
- discrete cosine transform, fast Fourier transform, pattern recognition, Wiener filter, orthogonal transformations, Karhunen-Loeve transform, rate-distortion performance, transform coding
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
