Data Compression Conference (DCC)

April 10 - April 12, 2012
(Reception on Monday evening, April 9;
presentations on Tuesday, Wednesday, and on Thursday morning.)

Snowbird, Utah
(All sessions to be held in the Cliff Lodge.)


Program Committee:

James A. Storer, Brandeis University (DCC Chair)
Michael W. Marcellin, University of Arizona (Committee Chair)
Henrique Malvar, Microsoft (Submissions Chair)
James E. Fowler, Mississippi State University (Publicity Chair)
Alberto Apostolico, Georgia Institute of Technology / Università di Padova
Ali Bilgin, University of Arizona
Charles D. Creusere, New Mexico State University
Vivek Goyal, Massachusetts Institute of Technology
Hamid Jafarkhani, University of California Irvine
Tamas Linder, Queen's University
Giovanni Motta, Google, Inc.
Gonzalo Navarro, University of Chile
Jan Ostergaard, Aalborg University
Majid Rabbani, Eastman Kodak Co.
Yuriy Reznik, Qualcomm
Thomas Richter, University of Stuttgart
Serap Savari, Texas A&M University
Khalid Sayood, University of Nebraska
Joan Serra-Sagrista, Universitat Autonoma Barcelona
Dana Shapira, Ashkelon Academic College
Dafna Sheinwald, IBM Haifa Lab
Marcelo Weinberger, HP Laboratories
Jiangtao Wen, Tsinghua University
Gregory W. Wornell, MIT
Feng Wu, Microsoft Research Asia


Theme:

An international forum for current work on data compression and related applications. The conference addresses not only compression methods for specific types of data (text, images, video, audio, medical, scientific, space, graphics, web content, etc.), but also the use of techniques from information theory and data compression in networking, communications, and storage applications involving large data sets, (including image and information mining, retrieval, archiving, backup, communications, and HCI). Both theoretical and experimental work are of interest. Topics of interest include but are not limited to: Lossless and lossy compression algorithms for specific types of data (text, images, multi-spectral and hyper-spectral images, palette images, video, speech, music, maps, instrument and sensor data, space data, earth observation data, graphics, 3D representations, animation, bit-maps, etc.), source coding, text compression, joint source-channel coding, multiple description coding, quantization theory, vector quantization (VQ), multiple description VQ, compression algorithms that employ transforms (including DCT and wavelet transforms), bi-level image compression, gray scale and color image compression, video compression, movie compression, geometry compression, speech and audio compression, compression of multi-spectral and hyper-spectral data, compression of science, weather, and space data, source coding in multiple access networks, parallel compression algorithms and hardware, fractal based compression methods, error resilient compression, adaptive compression algorithms, string searching and manipulation used in compression applications, closest-match retrieval in compression applications, browsing and searching compressed data, content based retrieval employing compression methods, steganography with respect to compressed data and the hiding of information in compressed data sets, minimal length encoding and applications to learning, system issues relating to data compression (including error control, data security, indexing, and browsing), medical imagery storage and transmission, compression of web graphs and related data structures, compression applications and issues for computational biology, compression applications and issues for the internet, compression applications and issues for mobile computing, applications of compression to file distribution and software updates, applications of compression to files storage and backup systems, applications of compression to data mining, applications of compression to information retrieval, applications of compression to image retrieval, applications of compression and information theory to human-computer interaction (HCI), applications of compression to bioinformatics, compression standards (including the JPEG, MPEG, H.xxx, and G.xxx families), compressed sensing / compressive sampling.