Image coding using wavelet transforms and entropy-constrained trellis coded quantization

Sriram, P.; Marcellin, Michael W.

doi:10.1109/icassp.1993.319871

Cited by 27 publications

(13 citation statements)

References 39 publications

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“…13(d). For comparison purposes, we give the most competitive compression results quoted recently in [30]- [32], although it should be noted that our results are first-order entropy estimates, while the others are measured in bits from a real coder. It can be seen that the space-frequency pruning algorithm compares favorably with the best approaches currently available.…”

Section: Methodsmentioning

confidence: 95%

Joint space-frequency segmentation using balanced wavelet packet trees for least-cost image representation

Herley

Xiong²,

Ramchandran³

et al. 1997

IEEE Trans. on Image Process.

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We examine the question of how to choose a space varying filterbank tree representation that minimizes some additive cost function for an image. The idea is that for a particular cost function, e.g., energy compaction or quantization distortion, some tree structures perform better than others. While the wavelet tree represents a good choice for many signals, it is generally outperformed by the best tree from the library of wavelet packet frequency-selective trees. The double-tree library of bases performs better still, by allowing different wavelet packet trees over all binary spatial segments of the image. We build on this foundation and present efficient new pruning algorithms for both one- and two-dimensional (1-D and 2-D) trees that will find the best basis from a library that is many times larger than the library of the single-tree or double-tree algorithms. The augmentation of the library of bases overcomes the constrained nature of the spatial variation in the double-tree bases, and is a significant enhancement in practice. Use of these algorithms to select the least-cost expansion for images with a rate-distortion cost function gives a very effective signal adaptive compression scheme. This scheme is universal in the sense that, without assuming a model for the signal or making use of training data, it performs very well over a large class of signal types. In experiments it achieves compression rates that are competitive with the best training-based schemes.

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Section: Methodsmentioning

confidence: 95%

Joint space-frequency segmentation using balanced wavelet packet trees for least-cost image representation

Herley

Xiong²,

Ramchandran³

et al. 1997

IEEE Trans. on Image Process.

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“…Our coding results will show that much improvement can be obtained with this new algorithm. Certainly, this algorithm can be combined with other more complicated methods such as methods using embedded zero-tree [25], [34] or trellis-coded quantization [29] to achieve better coding results.…”

Section: Image Coding Using Translation Invariant Wavelet Transformsmentioning

confidence: 99%

“…Image coding using wavelet transforms has been a very active research area in recent years [1], [23], [24], [25], [29], [34]. The plethora of interest can be contributed to the many good properties possessed by wavelet bases and wavelet transforms.…”

Section: Introductionmentioning

confidence: 99%

Image coding using translation invariant wavelet transforms with symmetric extensions

Liang

Parks²

1998

IEEE Trans. on Image Process.

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In this correspondence, we address the problem of translation sensitivity of conventional wavelet transforms for two-dimensional (2-D) signals. We propose wavelet transform algorithms that achieve the following desirable properties simultaneously: (i) translation invariance, (ii) reduced edge effects, and (iii) size-limitedness. We apply this translation invariant biorthogonal wavelet transform with symmetric extensions to image coding applications with good results.

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“…Some of the methods in the table, viz. [2], [4], and [5], are also based on trellis quantization. Unfortunately no information regarding the trellis state sizes is given in any of the papers.…”

Section: Resultsmentioning

confidence: 99%

“…The purpose of applying the transform is to find a representation suitable for compression (lossy or lossless). While most state-of-the-art methods are based on the increasingly popular wavelet transform (see, e-g., [I], [2], (31, (41, (5]), a different path has been chosen in this work.…”

Section: Introductionmentioning

confidence: 99%

Trellis Based Variable Rate Residual Image Coding over Noisy Channels

Eriksson

Goertz

Novak

et al.

Data Compression Conference (DCC'06)

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We consider the lossy image compression problem and propose a model-residual approach. Polynomial basis images encode the model image and powerful new trellis codes quantize the residual part. A simple bit allocation scheme determines the residual bit rates and a variety of rates are attainable without entropy coding. The trellis structure is also used to form a joint source and channel coding scheme for the residual components. Results are shown for the 0.4-1.6 bits per pixel region. Comparisons are made to several state-of-theart techniques and show that the proposed scheme is very competitive.Index Terms-Image coding, Trellis quantization, Model-residual approach, Joint source and channel coding.

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Image coding using wavelet transforms and entropy-constrained trellis coded quantization

Cited by 27 publications

References 39 publications

Joint space-frequency segmentation using balanced wavelet packet trees for least-cost image representation

Joint space-frequency segmentation using balanced wavelet packet trees for least-cost image representation

Image coding using translation invariant wavelet transforms with symmetric extensions

Trellis Based Variable Rate Residual Image Coding over Noisy Channels

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