Lossless image compression has become an important research topic,
especially in relation with the JPEG-LS standard. Recently, the techniques known for designing optimal codes for sources with infinite alphabets have been applied for the quantized Laplacian sources which have probability mass functions with two geometrically decaying tails. Due to the simple parametric model of the source distribution the Huffman iterations are possible to be carried out analytically, using the concept of reduced source, and the final codes are obtained as a sequence of very simple arithmetic operations, avoiding the need to store coding tables. We propose the use of these (optimal) codes in conjunction with context-based
prediction, for noiseless compression of images. To reduce further the average
code length, we design Escape sequences to be employed when the estimation
of the distribution parameter is unreliable.
Results on standard test files show improvements in compression ratio when comparing with JPEG-LS.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.