Improved set-partitioning embedded block scheme for still image compression

Shu-Mei Guo; Bing-Hui Wu; Jason Sheng-Hong Tsai

doi:10.1117/1.2952847

1 July 2008 Improved set-partitioning embedded block scheme for still image compression

Shu-Mei Guo, Bing-Hui Wu, Jason Sheng-Hong Tsai

Author Affiliations +

Journal of Electronic Imaging, Vol. 17, Issue 3, 033006 (July 2008). https://doi.org/10.1117/1.2952847

Abstract

In the last few years, wavelet-based still image compression schemes have become one of the most popular fields of research. Several powerful methods of encoding the wavelet coefficients have been proposed and set-partitioning embedded block (SPECK) is one of the most popular techniques. We propose a method called HSPECK (Huffman SPECK), which is based on SPECK to further improve the compression ratio. By observation, we are able to establish the probability distribution over the significance pattern of four subblocks generated successively along the wavelet decomposition process. Therefore, the Huffman coding scheme can then be employed for coding wavelet coefficients. The experimental results show that under the same peak signal-to-noise ratio, the bit rates via the proposed approach improve between 6.1% and 14.9%.

©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Shu-Mei Guo, Bing-Hui Wu, and Jason Sheng-Hong Tsai "Improved set-partitioning embedded block scheme for still image compression," Journal of Electronic Imaging 17(3), 033006 (1 July 2008). https://doi.org/10.1117/1.2952847

Published: 1 July 2008

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available