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In the field of image and data compression,there are always new approaches being tried and tested to improve the qualityof the reconstructed image and to reduce the computational complexity of thealgorithm employed. However, there is no one perfect technique that can offerboth maximum compression possible and best reconstruction quality, for any typeof image. Depending on the level of compression desired and characteristics ofthe input image, a suitable choice must be made from the options available. Forexample in the field of video compression, the integer adaptation of discretecosine transform DCT with fixed quantization is widely used in view of itsease of computation and adequate performance. There exist transforms like,discrete Tchebichef transform DTT, which are suitable too, but arepotentially unexploited. This work aims to bridge this gap and examine caseswhere DTT could be an alternative compression transform to DCT based on variousimage quality parameters. A multiplier-free fast implementation of integer DTTITT of size 8 × 8 is also studied, for its low computational complexity. Dueto the uneven spread of data across images, some areas might have intricatedetail, whereas others might be rather plain. This prompts the use of a compressionmethod that can be adapted according to the amount of detail. So, instead offixed quantization this paper employs quantization that varies depending on thecharacteristics of the image block. This implementation is free from additionalcomputational or transmission overhead. The image compression performance ofITT and ICT, using both variable and fixed quantization, is compared with avariety of images and the cases suitable for ITT-based image compressionemploying variable quantization are identified.

KEYWORDS

Discrete Tchebichef Transform DTT, Variable Quantization, Image Compression, Multiplier Free Implementation of ITT

Cite this paper

Prattipati, S. , Swamy, M. and Meher, P. 2015 A Comparison of Integer Cosine and Tchebichef Transforms for Image Compression Using Variable Quantization. Journal of Signal and Information Processing, 6, 203-216. doi: 10.4236-jsip.2015.63019.





Autor: Soni Prattipati1, M. N. S. Swamy1, Pramod K. Meher2

Fuente: http://www.scirp.org/



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