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Journal articles on the topic 'Discrete cosine transform'

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Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Radha Krishna A.N, Radha Krishna A. N. "JPEG Encoder using Discrete Cosine Transform & Inverse Discrete Cosine Transform." IOSR Journal of Electronics and Communication Engineering 5, no. 4 (2013): 51–56. http://dx.doi.org/10.9790/2834-0545156.

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2

Najiyah, Ina. "Analisis Perbandingan Discrete Cosine Transform Dan Discrete Wavelet Transform Untuk Kompresi Citra." Jurnal Responsif : Riset Sains dan Informatika 2, no. 1 (March 7, 2020): 106–17. http://dx.doi.org/10.51977/jti.v2i1.195.

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Kompresi citra atau image compression adalah sebuah metode untuk mengkompresi sebuah citra tanpa merusak citra aslinya dengan tujuan untuk memperkecil ukuran atau memory. Banyak metode yang diimplementasikan dalam bidang image compression ini dan metode-metode atau algoritma tersebut tentunya memiliki cara kerja dan hasil yang berbeda. Penelitian ini melakukan image compression dengan membandingkan metode Discreate Cosine Transform dan metode Discreate Wavelete Transform guna diketahui mana metode yang lebih baik dalam hal Image Compression. Dataset yang digunakan dalam penelitian ini berjumlah 30 dataset. Alat yang dipakai dalam mencari perbandingan Discreate Cosine Transform dan metode Discreate Wavelete Transform adalah MATLAB versi terbaru. Hasil dari penelitian ini yaitu didapatkan nilai PSNR, MSE dan SC dari masing-masing metode. Didapatkan metode yang paling baik adalah discreat wavelete transform dibanding metode Discreate Cosine Transform dengan nilai PSNR sebesar 57.3726 dB, nilai MSE sebesar 0.025593 dan nilai SC sebesar 98.0433. Kata Kunci: Kompresi Citra, Discreate Cosine Transform, Discreate Wavelete Transform

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3

Strang, Gilbert. "The Discrete Cosine Transform." SIAM Review 41, no. 1 (January 1999): 135–47. http://dx.doi.org/10.1137/s0036144598336745.

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4

Fracastoro, Giulia, Sophie M. Fosson, and Enrico Magli. "Steerable Discrete Cosine Transform." IEEE Transactions on Image Processing 26, no. 1 (January 2017): 303–14. http://dx.doi.org/10.1109/tip.2016.2623489.

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5

Chitprasert, B., and K. R. Rao. "Discrete cosine transform filtering." Signal Processing 19, no. 3 (March 1990): 233–45. http://dx.doi.org/10.1016/0165-1684(90)90115-f.

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6

Olkkonen, H. "Running discrete cosine transform." Journal of Biomedical Engineering 14, no. 6 (November 1992): 507–8. http://dx.doi.org/10.1016/0141-5425(92)90104-s.

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7

Cho, Min-Kyoung. "Discrete cosine transform domain image resizing using correlation of discrete cosine transform coefficients." Journal of Electronic Imaging 15, no. 3 (July 1, 2006): 033009. http://dx.doi.org/10.1117/1.2234736.

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8

Cariolaro, G., T. Erseghe, and P. Kraniauskas. "The fractional discrete cosine transform." IEEE Transactions on Signal Processing 50, no. 4 (April 2002): 902–11. http://dx.doi.org/10.1109/78.992138.

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9

Yuk-Hee Chan and Wan-Chi Siu. "Mixed-radix discrete cosine transform." IEEE Transactions on Signal Processing 41, no. 11 (1993): 3157–61. http://dx.doi.org/10.1109/78.257281.

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10

Skodras, A. N. "Fast discrete cosine transform pruning." IEEE Transactions on Signal Processing 42, no. 7 (July 1994): 1833–37. http://dx.doi.org/10.1109/78.298293.

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11

ZHELUDEV, VALERY A., DAN D. KOSLOFF, and EUGENE Y. RAGOZA. "COMPRESSION OF SEGMENTED 3D SEISMIC DATA." International Journal of Wavelets, Multiresolution and Information Processing 02, no. 03 (September 2004): 269–81. http://dx.doi.org/10.1142/s0219691304000536.

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We present a preliminary investigation of compression of segmented 3D seismic volumes for the rendering purposes. Promising results are obtained on the base of 3D discrete cosine transforms followed by the SPIHT coding scheme. An accelerated version of the algorithm combines 1D discrete cosine transform in vertical direction with the 2D wavelet transform of horizontal slices. In this case the SPIHT scheme is used for coding the mixed sets of cosine-wavelet coefficients.

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12

Tiwari, Anurag, Payal Chandrakant, Trip ti, and Surabhi Chaudhary. "Image Compression Using Discrete Cosine Transform & Discrete Wavelet Transform." International Journal of Web Technology 002, no. 001 (June 10, 2013): 25–27. http://dx.doi.org/10.20894/ijwt.104.002.001.007.

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13

Olkkonen, Hannu, Peitsa Pesola, and Juuso T. Olkkonen. "Computation of Hilbert Transform via Discrete Cosine Transform." Journal of Signal and Information Processing 01, no. 01 (2010): 18–23. http://dx.doi.org/10.4236/jsip.2010.11002.

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14

Jang, Ju-wook, Chang-hyeon Lim, Ronald Scrofano, and Viktor K. Prasanna. "Energy-Efficient Discrete Cosine Transform on FPGAs." KIPS Transactions:PartA 12A, no. 4 (August 1, 2005): 313–20. http://dx.doi.org/10.3745/kipsta.2005.12a.4.313.

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15

Kasparyan, M. S. "GENERALIZED DISCRETE ORTHOGONAL SINE-COSINE TRANSFORM." Computer Optics 38, no. 4 (January 1, 2014): 881–85. http://dx.doi.org/10.18287/0134-2452-2014-38-4-881-885.

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16

Sherlock, Barry G., and Donald M. Monro. "Algorithm 749: fast discrete cosine transform." ACM Transactions on Mathematical Software 21, no. 4 (December 1995): 372–78. http://dx.doi.org/10.1145/212066.212071.

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17

Naidu, V. "Discrete Cosine Transform-based Image Fusion." Defence Science Journal 60, no. 1 (January 24, 2010): 48–54. http://dx.doi.org/10.14429/dsj.60.105.

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18

Vetterli, M., and A. Ligtenberg. "A Discrete Fourier-Cosine Transform Chip." IEEE Journal on Selected Areas in Communications 4, no. 1 (January 1986): 49–61. http://dx.doi.org/10.1109/jsac.1986.1146289.

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19

Chan, S. C., and K. L. Ho. "Fast odd discrete cosine transform algorithms." Electronics Letters 26, no. 8 (1990): 503. http://dx.doi.org/10.1049/el:19900327.

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20

Lee, M. C., Raymond K. W. Chan, and Don A. Adjeroh. "Fast Three-Dimensional Discrete Cosine Transform." SIAM Journal on Scientific Computing 30, no. 6 (January 2008): 3087–107. http://dx.doi.org/10.1137/070693370.

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21

Zhongde Wang. "Pruning the fast discrete cosine transform." IEEE Transactions on Communications 39, no. 5 (May 1991): 640–43. http://dx.doi.org/10.1109/26.87153.

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22

Britaňák, Vladimír. "On the discrete cosine transform computation." Signal Processing 40, no. 2-3 (November 1994): 183–94. http://dx.doi.org/10.1016/0165-1684(94)90066-3.

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23

Agbinya, J. I. "Interpolation using the discrete cosine transform." Electronics Letters 28, no. 20 (1992): 1927. http://dx.doi.org/10.1049/el:19921233.

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24

Steidl, Gabriele. "Fast radix-p discrete cosine transform." Applicable Algebra in Engineering, Communication and Computing 3, no. 1 (March 1992): 39–46. http://dx.doi.org/10.1007/bf01189022.

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25

Boukaache, Abdennour. "Hybrid discrete cosine transform–discrete wavelet transform for progressive image compression." Journal of Electronic Imaging 21, no. 1 (February 27, 2012): 013006. http://dx.doi.org/10.1117/1.jei.21.1.013006.

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26

Balsa, Jose. "Comparison of Image Compressions: Analog Transformations." Proceedings 54, no. 1 (August 21, 2020): 37. http://dx.doi.org/10.3390/proceedings2020054037.

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A comparison between the four most used transforms, the discrete Fourier transform (DFT), discrete cosine transform (DCT), the Walsh–Hadamard transform (WHT) and the Haar-wavelet transform (DWT), for the transmission of analog images, varying their compression and comparing their quality, is presented. Additionally, performance tests are done for different levels of white Gaussian additive noise.

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27

O. Ahmed, Hawkar. "Race Classification from Face Images Using Fast Fourier Transform and Discrete Cosine Transform." Journal of Zankoy Sulaimani - Part A 22, no. 2 (December 20, 2020): 149–56. http://dx.doi.org/10.17656/jzs.10816.

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28

Song, Hyeonju, and Yung-Lyul Lee. "Inverse Transform Using Linearity for Video Coding." Electronics 11, no. 5 (March 1, 2022): 760. http://dx.doi.org/10.3390/electronics11050760.

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In hybrid block-based video coding, transform plays an important role in energy compaction. Transform coding converts residual data in the spatial domain into frequency domain data, thereby concentrating energy in a lower frequency band. In VVC (versatile video coding), the primary transform is performed using DCT-II (discrete cosine transform type 2), DST-VII (discrete sine transform type 7), and DCT-VIII (discrete cosine transform type 8). Considering that DCT-II, DST-VII, and DCT-VIII are all linear transforms, inverse transform is proposed to reduce the number of computations by using the linearity of transform. When the proposed inverse transform using linearity is applied to the VVC encoder and decoder, run-time savings can be achieved without decreasing the coding performance relative to the VVC decoder. It is shown that, under VVC common-test conditions (CTC), average decoding time savings values of 4% and 10% are achieved for all intra (AI) and random access (RA) configurations, respectively.

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29

Ahmad, Faraidoon H., and Aree A. Mohammed. "Hybrid Wavelet and Discrete Cosine Transform Methods for Ethnicity Identification." Journal of Zankoy Sulaimani - Part A 17, no. 1 (December 10, 2014): 121–30. http://dx.doi.org/10.17656/jzs.10366.

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30

Zhang, Xiangfen, Hongfeng Wu, and Yan Ma. "A new auto-focus measure based on medium frequency discrete cosine transform filtering and discrete cosine transform." Applied and Computational Harmonic Analysis 40, no. 2 (March 2016): 430–37. http://dx.doi.org/10.1016/j.acha.2015.08.008.

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31

Zhu, Gui Liang, Xiao Li Qin, and Xiao Qiang Zhang. "Image Encryption Algorithm Based on Image Element and DFRCT." Advanced Materials Research 532-533 (June 2012): 1403–8. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1403.

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A discrete fractional cosine transform with arbitrary period is proposed, which is a linear combination of discrete fractional cosine transform (DFRCT). We propose a new image encryption algorithm based on the discrete fractional cosine transform with arbitrary period, and set up some experiments by the different image block methods. The specific steps of encryption and decryption are given, and this encryption algorithm in items of many evaluation indexes is analyzed. The results of the detailed analysis demonstrate that the encryption algorithm effectiveness, efficiency and ability to resist brute-force attack are satisfying.

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32

Rolon-Heredia, J. A., V. M. Garrido-Arevalo, and J. Marulanda. "Voice compression using discrete cosine transform and wavelet transform." Journal of Physics: Conference Series 1403 (November 2019): 012001. http://dx.doi.org/10.1088/1742-6596/1403/1/012001.

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33

Faruqi, Ahmad Adil, and Imam Fahrur Rozi. "IMPLEMENTASI STEGANOGRAPHY MENGGUNAKAN ALGORITMA DISCRETE COSINE TRANSFORM." Jurnal Informatika Polinema 2, no. 1 (March 16, 2017): 35. http://dx.doi.org/10.33795/jip.v2i1.52.

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Steganography merupakan metode yang berbeda dengan metode kriptografi yang mengubah sebuah informasi kedalam sebuah sandi sehingga tidak dapat dibaca tanpa mengetahui kunci atau sandi yang digunakan, namun keberadaannya tetap diketahui dan tidak disembunyikan. Sedangkan pada steganography informasi akan disembunyikan didalam suatu media pembawa sehingga tak seorangpun yang menyadari bahwa terdapat suatu informasi. Algoritma yang digunakan dalam steganography ini adalah algoritma discrete cosine transform. Steganography menggunakan algoritma discrete cosine transform menghasilkan gambar steganography dengan memiliki kualitas yang tidak jauh berbeda dari gambar aslinya hal ini ditunjukkan dengan besarnya nilai rata-rata hasil peak signal to noise ratio (PSNR) dari tiga gambar yang berbeda yaitu sebesar 37.44 db. Hasil pengujian kompresi gambar steganography, menyimpulkan bahwa pesan dalam gambar hasil steganography menggunakan algoritma discrete cosine transform tahan terhadap kompresi gambar.

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34

Tang, Ling, Ming Ju Chen, and Hong Song. "Hybrid Color Image Compression Using Discrete Cosine Transform and Discrete Wavelet Transform." Applied Mechanics and Materials 198-199 (September 2012): 244–48. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.244.

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In this research we undertake a study of image compression based on the discrete cosine transform(DCT) and discrete wavelet transform(DWT). Then a hybrid color image compression algorithm based on DCT and DWT is proposed. This algorithm is implemented through transform the color image using DWT in the YCbCr space first, and then DCT in the low frequency, adopt huffman coding, RLE and arithmetic coding in the encoded mode. In experiments, the results outperform the only DCT and the only DWT typically higher in peak signal-to-noise ratio and have better visual quality.

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35

Malvar, H. "Fast computation of the discrete cosine transform and the discrete Hartley transform." IEEE Transactions on Acoustics, Speech, and Signal Processing 35, no. 10 (October 1987): 1484–85. http://dx.doi.org/10.1109/tassp.1987.1165043.

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36

Britaňák, Vladimir. "A unified discrete cosine and discrete sine transform computation." Signal Processing 43, no. 3 (May 1995): 333–39. http://dx.doi.org/10.1016/0165-1684(95)00010-b.

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37

Davies, Robert B. "Integrated processes and the discrete cosine transform." Journal of Applied Probability 38, A (2001): 105–21. http://dx.doi.org/10.1239/jap/1085496595.

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A time-series consisting of white noise plus Brownian motion sampled at equal intervals of time is exactly orthogonalized by a discrete cosine transform (DCT-II). This paper explores the properties of a version of spectral analysis based on the discrete cosine transform and its use in distinguishing between a stationary time-series and an integrated (unit root) time-series.

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38

Davies, Robert B. "Integrated processes and the discrete cosine transform." Journal of Applied Probability 38, A (2001): 105–21. http://dx.doi.org/10.1017/s0021900200112719.

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A time-series consisting of white noise plus Brownian motion sampled at equal intervals of time is exactly orthogonalized by a discrete cosine transform (DCT-II). This paper explores the properties of a version of spectral analysis based on the discrete cosine transform and its use in distinguishing between a stationary time-series and an integrated (unit root) time-series.

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39

Ta, N., Y. Attikiouzel, and G. Crebbin. "Fast computation of two-dimensional discrete cosine transforms using fast discrete radon transform." Electronics Letters 27, no. 1 (January 3, 1991): 82–84. http://dx.doi.org/10.1049/el:19910053.

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40

Barros, Benjamin, and Brody Dylan Johnson. "Sparse Recovery Using the Discrete Cosine Transform." Journal of Geometric Analysis 31, no. 9 (January 29, 2021): 8991–98. http://dx.doi.org/10.1007/s12220-020-00574-0.

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41

Begum, Mahbuba, and Mohammad Shorif Uddin. "Multiple Image Watermarking with Discrete Cosine Transform." Journal of Computer and Communications 09, no. 03 (2021): 88–94. http://dx.doi.org/10.4236/jcc.2021.93006.

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42

Ramesh, D., D. Krishna, and K. Rajasekhar. "Removing Camera Shake using Discrete Cosine Transform." International Journal of Computer Applications 170, no. 9 (July 17, 2017): 7–10. http://dx.doi.org/10.5120/ijca2017914801.

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43

Jafarpour, Behnam, and Dennis B. McLaughlin. "Reservoir Characterization With the Discrete Cosine Transform." SPE Journal 14, no. 01 (March 1, 2009): 182–201. http://dx.doi.org/10.2118/106453-pa.

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44

Prashantha, Dr H. S. "MULTISPECTRAL IMAGE COMPRESSION USING DISCRETE COSINE TRANSFORM." International Journal of Engineering Applied Sciences and Technology 5, no. 3 (July 31, 2020): 266–70. http://dx.doi.org/10.33564/ijeast.2020.v05i03.042.

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45

Lukin, V. V., R. Oktem, N. N. Ponomarenko, and Karen O. Egiazarian. "Image Filtering Based on Discrete Cosine Transform." Telecommunications and Radio Engineering 66, no. 18 (2007): 1685–701. http://dx.doi.org/10.1615/telecomradeng.v66.i18.70.

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46

Takala, J., D. Akopian, J. Astola, and J. Saarinen. "Constant geometry algorithm for discrete cosine transform." IEEE Transactions on Signal Processing 48, no. 6 (June 2000): 1840–43. http://dx.doi.org/10.1109/78.845951.

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47

Kasezawa, T. "Blocking artifacts reduction using discrete cosine transform." IEEE Transactions on Consumer Electronics 43, no. 1 (1997): 48–55. http://dx.doi.org/10.1109/30.580384.

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48

Shukla, Dolley, and Manisha Sharma. "Improved Video Watermarking using Discrete Cosine Transform." i-manager's Journal on Image Processing 2, no. 3 (September 15, 2015): 29–34. http://dx.doi.org/10.26634/jip.2.3.3603.

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49

Ja-Ling Wu and Jiun Shin. "Discrete cosine transform in error control coding." IEEE Transactions on Communications 43, no. 5 (May 1995): 1857–61. http://dx.doi.org/10.1109/26.387423.

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50

Jar-Ferr Yang and Chih-Peng Fan. "Compact recursive structures for discrete cosine transform." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 47, no. 4 (April 2000): 314–21. http://dx.doi.org/10.1109/82.839667.

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