Development of a method for compressing images on the basis of JPEG algorithm
DOI:
https://doi.org/10.15587/2706-5448.2020.202433Keywords:
compression method, image optimization, JPEG algorithm, image quality, image size.Abstract
The problem of image optimization, namely the reduction of the physical size of the image by minimizing image quality as little as possible, is considered. The object of research are methods for processing and compressing images. When analyzing the methods, one of the biggest problems was discovered, which consists in the fact that when solving the problem of image processing and compression, the studied methods allow to achieve the slightest loss in quality, but as a result, the compression ratio is significantly reduced. To overcome this problem, it was decided to develop a modification of the JPEG compression algorithm. The proposed modification consists in additional quantization of the spectrum after a discrete cosine transform, and then the resulting spectrum is fed to a Huffman encoder, which makes compression even more efficient. A method is obtained for solving the image optimization problem, which allows one to obtain an image with a smaller size and a large compression ratio while maintaining optimal quality. This is due to the fact that the proposed method has a number of features, as the original color image can have 24 bits per point, in particular, the ability to set the compression ratio. Thanks to this, it is possible to obtain a signal-to-noise ratio of 54.2 dB at a quality factor of zero. Compared with the well-known LZW algorithm, which is much better, as a result of which it allows to get a processed image with a much smaller physical size. The assessment of image quality, depending on the parameters of the task. It is shown that for problems of small and medium dimensions, the developed method provides minimal quality loss. The results of solving the problem for a specific example demonstrate the advantage of the developed method over existing ones. The results can be successfully applied to solve the problem of optimizing image size while maintaining maximum qualityReferences
- David, S. (2004). Image Compression. Data Compression. New York: Springer-Verlag, 251–512. doi: http://doi.org/10.1007/0-387-21832-7_5
- Gray, R. M. (1991). Image compression. Data Compression Conference. Snowbird. doi: http://doi.org/10.1109/dcc.1991.213293
- Howard, P. G., Vitter, J. S. (1992). Parallel lossless image compression using Huffman and arithmetic coding. Data Compression Conference. doi: http://doi.org/10.1109/dcc.1992.227451
- Ansari, R., Memon, N., Tseran, E. (1998). Image lossless compression methods. Journal of Electronic Imaging, 7 (3), 486–494. doi: http://doi.org/10.1117/1.482591
- Horspool, R. N. (1991). Improving LZW (data compression algorithm). Data Compression Conference. doi: http://doi.org/10.1109/dcc.1991.213347
- Salari, E., Whyte, W. A. (1991). Compression of stereoscopic image data. Data Compression Conference. doi: http://doi.org/10.1109/dcc.1991.213336
- Halder, A., Banerjee, A. (2010). An efficient image compression algorithm for almost dual-color image based on k-means clustering, bit-map generation and RLE. International Conference on Computer and Communication Technology (ICCCT). doi: http://doi.org/10.1109/iccct.2010.5640529
- Firas, A. J., Hind, E. Q. (2012). Five Modulus Method for Image Compression. Signal & Image Processing : An International Journal, 3 (5), 19–28. doi: http://doi.org/10.5121/sipij.2012.3502
- Wu, X., Memon, N. (1997). Context-based, adaptive, lossless image coding. IEEE Transactions on Communications, 45 (4), 437–444. doi: http://doi.org/10.1109/26.585919
- Tinku, A., Ping-Sing, T. (2005). JPEG – Still Image Compression Standard. JPEG2000 Standard for Image Compression. Springer, 55–78. doi: http://doi.org/10.1002/0471653748.ch3
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Copyright (c) 2020 Ievgen Fedorchenko, Andrii Oliinyk, Alexander Stepanenko, Serhii Korniienko, Anastasia Kharchenko, Valerii Laktionov
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