The Role of Transforms in Image Compression

被引：9

作者：

Bairagi V.K. ^{[1
]}

Sapkal A.M. ^{[2
]}

Gaikwad M.S. ^{[1
]}

机构：

[1] Department of Electronics and Telecommunication, Sinhgad Academy of Engineering, Kondhwa (Bk.), Pune, 411048, Maharashtra

[2] Department of Electronics and Telecommunication Engineering, College of Engineering, Pune, Shivajinagar, Pune, 411005, Maharashtra

来源：

Journal of The Institution of Engineers (India): Series B | 2013年 / 94卷 / 2期

关键词：

Entropy; Image transforms; Lossless compression; Wavelet transform;

D O I：

10.1007/s40031-013-0049-9

中图分类号：

学科分类号：

摘要：

In today’s multimedia wireless communication, major issue is bandwidth needed to satisfy real time transmission of image data. Compression is one of the good solutions to address this issue. Transform based compression algorithms are widely used in the field of compression, because of their de-correlation and other properties, useful in compression. In this paper, comparative study of compression methods is done based on their types. This paper addresses the issue of importance of transform in image compression and selecting particular transform for image compression. A comparative study of performance of a variety of different image transforms is done base on compression ratio, entropy and time factor. © 2013, The Institution of Engineers (India).

引用

页码：135 / 140

页数：5

共 17 条

[1]

Miaou S.-G., Ke F.-S., Chen S.-C., A lossless compression method for medical image sequences using JPEG-LS and interframe coding, IEEE Trans. Inf Technol. Biomed., 13, 5, pp. 818-821, (2009)

[2]

Placidi G., Adaptive compression algorithm from projections: application on medical greyscale images, J. Comput. Biol. Med., 39, pp. 993-999, (2009)

[3]

Gonzalez R.C., Woods R.E., Digital Image Processing, (2010)

[4]

Seymour R., Stewart D., Ming J., Comparison of image transform-based features for visual speech recognition in clean and corrupted videos, EURASIP J. Image Video Process., 2008, (2008)

[5]

Jain A.K., Fundamentals of Digital Image Processing, Eaglewood Cliffs, (2001)

[6]

Amar A., Leshem A., Gastpar M., Recursive implementation of the distributed Karhunen-Loève transform, IEEE Trans. Signal Process., 58, 10, pp. 5320-5330, (2010)

[7]

Makkaoui L., Lecuire V., Moureaux J.-M., Fast zonal DCT-based image compression for wireless camera sensor networks, IEEE Signal Process. Lett., 14, 2, pp. 105-108, (2010)

[8]

Ouyang W., Cham W.-K., Fast algorithm for Walsh Hadamard transform on sliding windows, IEEE Trans. Pattern Anal. Mach. Intell., 32, 1, pp. 165-171, (2010)

[9]

Wu X., Qiu T., Wavelet coding of volumetric medical images for high throughput and operability, IEEE Trans. Med. Imaging, 24, 6, pp. 719-727, (2005)

[10]

Soman K.P., Ramachandran K.I., Resmi N.G., Insight into wavelets from theory to practice, pp. 153-194, (2009)

← 1 2 →