2026-09-19T03:25:41Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1015832022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Jae S. Lim.
Nissenbaum, Lucas
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-03-03T21:10:26Z
2016-03-03T21:10:26Z
2015
2015
http://hdl.handle.net/1721.1/101583
940973860
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 75-77).
In video compression, a single transform such as the DCT is typically used. Multiple-transforms such as directional 1-D DCTs have been proposed to exploit the different statistical characteristics of motion compensation residuals. Many issues are associated with this scenario. In this thesis, we will focus on the issue of selecting the appropriate number of coefficients and transforms to be allocated for each block of the signal to optimize the energy compaction. We propose two new methods to select optimal transforms for different blocks in a signal. The first method is based on thresholding, while the second method is based on dynamic programming and related to the multiple-choice knapsack problem. These algorithms are then compared to two other previous algorithms. We analyze the energy compaction performance of these algorithms in terms of different block-sizes and different input characteristics. We then extend all of these algorithms to quantizated coefficients being transmitted, as well as to take a bit-rate constraint into account.
by Lucas Nissenbaum.
S.M.
77 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Globally optimal algorithms for multiple-transform signal compression
Thesis
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