Transforms for prediction residuals in video coding

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.

Bibliographic Details
Main Author: Kamışlı, Fatih
Other Authors: Jae S. Lim.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2011
Subjects:
Online Access:http://hdl.handle.net/1721.1/62424
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author Kamışlı, Fatih
author2 Jae S. Lim.
author_facet Jae S. Lim.
Kamışlı, Fatih
author_sort Kamışlı, Fatih
collection MIT
description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
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spelling mit-1721.1/624242019-04-09T16:37:17Z Transforms for prediction residuals in video coding Kamışlı, Fatih Jae S. Lim. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010. Cataloged from PDF version of thesis. Includes bibliographical references (p. 135-140). Typically the same transform, the 2-D Discrete Cosine Transform (DCT), is used to compress both image intensities in image coding and prediction residuals in video coding. Major prediction residuals include the motion compensated prediction residual, the resolution enhancement residual in scalable video coding, and the intra prediction residual in intra-frame coding. The 2-D DCT is efficient at decorrelating images, but the spatial characteristics of prediction residuals can be significantly different from the spatial characteristics of images, and developing transforms that are adapted to the characteristics of prediction residuals can improve their compression efficiency. In this thesis, we explore the differences between the characteristics of images and prediction residuals by analyzing their local anisotropic characteristics and develop transforms adapted to the local anisotropic characteristics of some types of prediction residuals. The analysis shows that local regions in images have 2-D anisotropic characteristics and many regions in several types of prediction residuals have 1-D anisotropic characteristics. Based on this insight, we develop 1-D transforms for these residuals. We perform experiments to evaluate the potential gains achievable from using these transforms within the H.264 codec, and the experimental results indicate that these transforms can increase the compression efficiency of these residuals. by Fatih Kamışlı. Ph.D. 2011-04-25T15:56:36Z 2011-04-25T15:56:36Z 2010 2010 Thesis http://hdl.handle.net/1721.1/62424 710987360 eng 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 140 p. application/pdf Massachusetts Institute of Technology
spellingShingle Electrical Engineering and Computer Science.
Kamışlı, Fatih
Transforms for prediction residuals in video coding
title Transforms for prediction residuals in video coding
title_full Transforms for prediction residuals in video coding
title_fullStr Transforms for prediction residuals in video coding
title_full_unstemmed Transforms for prediction residuals in video coding
title_short Transforms for prediction residuals in video coding
title_sort transforms for prediction residuals in video coding
topic Electrical Engineering and Computer Science.
url http://hdl.handle.net/1721.1/62424
work_keys_str_mv AT kamıslıfatih transformsforpredictionresidualsinvideocoding