Estudo e Comparação de Técnicas de Compressão de Imagens Baseadas em Transformadas Discretas
Abstract
This work aims to study and compare methods, levels and forms of image compression based on the masking and quantization of the transform used by the JPEG standard, the Discrete Cosine Transform (DCT). For this, experiments were performed with selected images from five categories: landscape, portrait, typography, geometric patterns and social context. Compression effectiveness and image quality were evaluated in terms of statistical metrics such as Entropy, PSNR and UIQI, in order to obtain the best processing configuration for each image category. During the tests, it was found that the DCT compression method via quantization tables performed better than the masking of DCT coefficients. Based on this, we propose a new set of quantization tables for the DCT called KDN, which obtained the best overall performance, even outperforming the traditional JPEG standard quantization tables.
References
R. Gonzalez and R. Woods, Digital Image Processing. Prentice Hall, 2002. [Online]. Available: https://books.google.com.br/books?id=738oAQAAMAAJ
W.-D. Liang and X.-D. Liu, "Comparison of approximate dct and approximate dtt for image compression," in 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), 2021, pp. 337-341.
A. C. Bovik, Handbook of Image and Video Processing, ser. Communications, Networking and Multimedia. Elsevier Science, 2010. [Online]. Available: https://books.google.com.br/books?id=UM_GCfJe88sC
G. J. Sullivan and J.-R. Ohm, "Recent developments in standardization of high efficiency video coding (HEVC)," in Applications of Digital Image Processing XXXIII, A. G. Tescher, Ed., vol. 7798, International Society for Optics and Photonics. SPIE, 2010, pp. 239 - 245. [Online]. Available: https://doi.org/10.1117/12.863486
J. Lainema, M. M. Hannuksela, V. K. M. Vadakital, and E. B. Aksu, "Hevc still image coding and high efficiency image file format," in 2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 71-75.
C.-Y. Pang, R.-G. Zhou, B.-Q. Hu, W. Hu, and A. El-Rafei, "Signal and image compression using quantum discrete cosine transform," Information Sciences, vol. 473, pp. 121-141, 2019. [Online]. Available: [link]
J. C. Russ, The Image Processing Handbook. CRC Press, 6ed., 2011.
International Telecommunication Union, Information technology - Digital compression and coding of continuous-tone still images: Requirements and guidelines, 1994.
A. J. I. Barbhuiya, T. A. Laskar, and K. Hemachandran, "An approach for color image compression of jpeg and png images using dct and dwt," in 2014 International Conference on Computational Intelligence and Communication Networks, 2014, pp. 129-133.
G. Stolfi, COMPRESSÃO DE IMAGENS: PADRÃO JPEG, Notas de aula da disciplina PTC2547 - PRINCÍPIOS DE TELEVISÃO DIGITAL, da EPUSP, 2016. [Online]. Available: http://www.lcs.poli.usp.br/~gstolfi/PPT/APTV0616.pdf
J. Hwang, Multimedia Networking: From Theory to Practice. Cambridge, 2009.
Z. Wang and A. Bovik, "A universal image quality index," IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81-84, 2002.
D.-Y. Tsai, Y. Lee, and E. Matsuyama, "Information-entropy measure for evaluation of image quality," Journal of Digital Imaging, vol. 21, 09 2008.
E. Hamilton, JPEG File Interchange Format, 09 1992. [Online]. Available: https://www.w3.org/Graphics/JPEG/jfif3.pdf
F. Zhang and D. Bull, Intelligent Image and Video Compression: Communicating Pictures. Elsevier Science, 2021. [Online]. Available: https://books.google.com.br/books?id=KiMZEAAAQBAJ
