TITLE:
A Comparison of Integer Cosine and Tchebichef Transforms for Image Compression Using Variable Quantization
AUTHORS:
Soni Prattipati, M. N. S. Swamy, Pramod K. Meher
KEYWORDS:
Discrete Tchebichef Transform (DTT), Variable Quantization, Image Compression, Multiplier Free Implementation of ITT
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.6 No.3,
July
21,
2015
ABSTRACT: In the field of image and data compression,
there are always new approaches being tried and tested to improve the quality
of the reconstructed image and to reduce the computational complexity of the
algorithm employed. However, there is no one perfect technique that can offer
both maximum compression possible and best reconstruction quality, for any type
of image. Depending on the level of compression desired and characteristics of
the input image, a suitable choice must be made from the options available. For
example in the field of video compression, the integer adaptation of discrete
cosine transform (DCT) with fixed quantization is widely used in view of its
ease of computation and adequate performance. There exist transforms like,
discrete Tchebichef transform (DTT), which are suitable too, but are
potentially unexploited. This work aims to bridge this gap and examine cases
where DTT could be an alternative compression transform to DCT based on various
image quality parameters. A multiplier-free fast implementation of integer DTT
(ITT) of size 8 × 8 is also studied, for its low computational complexity. Due
to the uneven spread of data across images, some areas might have intricate
detail, whereas others might be rather plain. This prompts the use of a compression
method that can be adapted according to the amount of detail. So, instead of
fixed quantization this paper employs quantization that varies depending on the
characteristics of the image block. This implementation is free from additional
computational or transmission overhead. The image compression performance of
ITT and ICT, using both variable and fixed quantization, is compared with a
variety of images and the cases suitable for ITT-based image compression
employing variable quantization are identified.