NDLI: Sequential prediction and ranking in universal context modeling and data compression

Content Provider	IEEE Xplore Digital Library
Author	Weinberger, M.J. Seroussi, G.
Copyright Year	1963
Abstract	Most state-of-the-art lossless image compression schemes use prediction followed by some form of context modeling. This might seem redundant at first, as the contextual information used for prediction is also available for building the compression model, and a universal coder will eventually learn the "predictive" patterns of the data. In this correspondence, we provide a format justification to the combination of these two modeling tools, by showing that a combined scheme may result in faster convergence rate to the source entropy. This is achieved via a reduction in the model cost of universal coding. In deriving the main result, we develop the concept of sequential ranking, which can be seen as a generalization of sequential prediction, and we study its combinatorial and probabilistic properties.
Sponsorship	IEEE Information Theory Society
Starting Page	1697
Ending Page	1706
Page Count	10
File Size	422809
File Format	PDF
ISSN	00189448
Volume Number	43
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-09-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context modeling Data compression Image coding Predictive models Convergence Entropy Costs Decorrelation Probability distribution Prediction algorithms
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Library and Information Sciences Information Systems

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Sequential prediction and ranking in universal context modeling and data compression (1997)

An Algorithm for Universal Lossless Compression With Side Information

Universal Estimation of Erasure Entropy

A lossless image coder with context-based minimizing MSE prediction and entropy coding

Efficient universal lossless data compression algorithms based on a greedy sequential grammar transform .2. With context models

On the interaction between universal context modeling and prediction

Universal Enumerative Coding for Tree Models

Gradient based selective weighting of neighboring pixels for predictive lossless image coding

Efficient universal lossless data compression algorithms based on a greedy sequential grammar transform. I. Without context models

Sequential prediction and ranking in universal context modeling and data compression

Similar Documents

Sequential prediction and ranking in universal context modeling and data compression (1997)

An Algorithm for Universal Lossless Compression With Side Information

Universal Estimation of Erasure Entropy

A lossless image coder with context-based minimizing MSE prediction and entropy coding

Efficient universal lossless data compression algorithms based on a greedy sequential grammar transform .2. With context models

On the interaction between universal context modeling and prediction

Universal Enumerative Coding for Tree Models

Gradient based selective weighting of neighboring pixels for predictive lossless image coding

Efficient universal lossless data compression algorithms based on a greedy sequential grammar transform. I. Without context models

Sequential prediction and ranking in universal context modeling and data compression