Algorithms for Transform Selection in Multiple-Transform Video Compression

With a proper transform, an image or motion-compensated residual can be represented quite accurately with a small fraction of the transform coefficients. This is referred to as the energy compaction property. When multiple transforms are used, selecting the best transform for each block that leads t...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 22; no. 12; pp. 5395 - 5407
Main Authors: Xun Cai, Lim, Jae S.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.12.2013
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied sciences
Coding, codes
Compaction
Data compression
Detection, estimation, filtering, equalization, prediction
Discrete cosine transforms
energy compaction
Energy measurement
Entropy coding
Exact sciences and technology
Image processing
Information, signal and communications theory
iterative algorithms
optimization
Quantization (signal)
Signal and communications theory
Signal processing
Signal, noise
Telecommunications and information theory
Transforms
Video compression
Video coding
Performance evaluation
Image processing
Data compression
Transforms
Iterative method
Image compression
Two dimensional model
Compaction
Algorithm
Optimization
Video signal processing
Image coding
Algorithm performance
energy compaction
Residual impurity
Optimal solution
One dimensional model
Signal processing
Motion compensation
Discrete cosine transforms
iterative algorithms
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Description
Summary:With a proper transform, an image or motion-compensated residual can be represented quite accurately with a small fraction of the transform coefficients. This is referred to as the energy compaction property. When multiple transforms are used, selecting the best transform for each block that leads to the best energy compaction is difficult. In this paper, we develop two algorithms to solve this problem. The first algorithm, which is computationally simple, leads to a locally optimal solution. The second algorithm, which is more computationally intensive, gives a globally optimal solution. We discuss the algorithms and their performance. Two-dimensional discrete cosine transform (2D-DCT) and direction-adaptive one-dimensional discrete cosine transforms (1D-DCTs) are used to evaluate the performance of our algorithms. Results obtained are consistent with their coding performance. As an application example of this paper, we apply our algorithm to evaluate the performance of a potential video compression system based on a very large number of transforms.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2013.2284073