By Topic

High performance wavelet image compression optimized for MSE and HVS metrics

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Topiwala, P. ; Mitre Corp., Bedford, MA, USA

Summary form only. Wavelet still image compression has been a focus of intense research. Considerable coding gains over older DCT-based methods have been achieved, while the computational complexity has been made very competitive. We report on a high performance wavelet still image compression algorithm optimized for both mean-squared error (MSE) and human visual system (HVS) characteristics. We present the problem of optimal quantization from a Lagrange multiplier point of view, and derive novel solutions. Ideally, all three components of a typical image compression system: transform, quantization, and entropy coding, should be optimized simultaneously. However, the highly nonlinear nature of quantization and encoding complicates the formulation of the total cost function. We consider optimizing the filter, and then the quantizer, separately, holding the other two components fixed. While optimal bit allocation has been treated in the literature, we specifically address the issue of setting the quantization step sizes, which in practice is quite different. We select a short high-performance filter, develop an efficient scalar MSE-quantizer, and four HVS-motivated quantizers which add some value visually without incurring any MSE losses. A combination of run-length and empirically optimized Huffman coding is fixed in this study

Published in:

Data Compression Conference, 1996. DCC '96. Proceedings

Date of Conference:

Mar/Apr 1996