Parameter reduction and context selection for compression of gray-scale images

Todd, Stephen; Langdon, Glen G.; Rissanen, Jorma

doi:10.1147/rd.292.0188

Cited by 83 publications

(36 citation statements)

References 5 publications

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“…3a. The prediction errors were bucketed (similar to [17]) into -zero,‖ -small‖ (1-2), -medium‖ (3-8), and -large‖ (over 9), with the sign of the error also used in the context. Since the context is computed from three prediction errors with seven possible values each, the coder uses 343 contexts.…”

Section: Coding For Multispectral Imagesmentioning

confidence: 99%

Band ordering in lossless compression of multispectral images

Tate

1997

IEEE Trans. Comput.

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(c) 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Abstract:In this paper, we consider a model of lossless image compression in which each band of a multispectral image is coded using a prediction function involving values from a previously coded band of the compression, and examine how the ordering of the bands affects the achievable compression.We present an efficient algorithm for computing the optimal band ordering for a multispectral image. This algorithm has time complexity O(n 2 ) for an n-band image, while the naive algorithm takes time (n!). A slight variant of the optimal ordering problem that is motivated by some practical concerns is shown to be NP-hard, and hence, computationally infeasible, in all cases except for the most trivial possibility.In addition, we report on our experimental findings using the algorithms designed in this paper applied to real multispectral satellite data. The results show that the techniques described here hold great promise for application to real-world compression needs.Index Terms-Compression, lossless compression, image compression, multispectral images, satellite data, NPcompleteness. Article:1 INTRODUCTION MULTISPECTRAL satellite images require enormous amounts of space, and with NASA's project EOS (the Earth Observing System), data will be generated at an unprecedented rate. The estimates are that over a terabyte (10 12 bytes) of data will be generated every day by the EOS satellites, most of it multispectral image data. Largely due to this fact, a lot of attention has recently been focused on compression of multispectral images [5]. However, most of the compression methods that exploit spectral as well as spatial redundancy have been lossy compression algorithms, and for archival storage and for certain applications it is important to use lossless compression in order to preserve all of the data that is collected. One notable exception to this is the work of Roger and Cavenor [4] who extensively study various prediction and coding methods used for lossless compression of AVIRIS data. Table 1 lists some current, widely used multispectral sources, with their acronyms, full names, and basic properties-it is data from these sensors that we used in this study.In this paper, we study lossless compression of multispectral images. Spectral redundancy is extracted by coding each band of the multispectral image by making use of a second -prediction band.‖ In much the same way that standard single-image lossless compression is separated into the two separate components of prediction and coding, we divide the lossless compression of multispectral images into three components: band ordering, prediction, and coding. The new stage, band ordering, refers to selecting a permutation...

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Section: Coding For Multispectral Imagesmentioning

confidence: 99%

Band ordering in lossless compression of multispectral images

Tate

1997

IEEE Trans. Comput.

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“…This step is often referred to as error modeling [5]. The error modeling techniques employed by most lossless compression schemes proposed in the literature can be captured within the context modeling framework described in [10] and applied in [5,12]. In this approach, the prediction error at each sample is encoded with respect to a conditioning state or context, which is computed from the values of previously encoded neighboring samples.…”

Section: Activity-based Conditional Codingmentioning

confidence: 99%

“…Viewed in this framework, the role of an error model is essentially to provide estimates of the conditional probability of the prediction error, given the context in which it occurs. This can be done by estimating the probability density function (pdf) by maintaining counts of sample occurrences within each conditioning state [12], or by estimating the parameters (variance, for example) of an assumed pdf (Laplacian, for example) as in [5]. We adopt the former approach here.…”

Section: Activity-based Conditional Codingmentioning

confidence: 99%

Context-based lossless and near-lossless compression of EEG signals

Memon

Kong

Cinkler

1999

IEEE Trans. Inform. Technol. Biomed.

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In this paper we study compression techniques for electroencephalograph (EEG) signals. A variety of lossless compression techniques, including compress, gzip, bzip, shorten, and several predictive coding methods, are investigated and compared. The methods range from simple dictionary based approaches to more sophisticated context modeling techniques. It is seen that compression ratios obtained by lossless compression are limited even with sophisticated context-based bias cancellation and activity-based conditional coding. Though lossy compression can yield significantly higher compression ratios while potentially preserving diagnostic accuracy, it is not usually employed due to legal concerns. Hence, we investigate a near-lossless compression technique that gives quantitative bounds on the errors introduced during compression. It is observed that such a technique gives significantly higher compression ratios (up to 3-bit per sample saving with less than 1% error). Compression results are reported for EEGs recorded under various clinical conditions.

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“…16 and applied in Refs. 22,7. In this approach, the prediction error at each pixel is encoded with respect to a conditioning state or context, which is arrived at from the values of previously encoded neighboring pixels.…”

Section: Near-lossless Compression Based On Predictive Coding Techniquesmentioning

confidence: 99%

“…Viewed in this framework, the role of the error model is essentially to provide estimates of the conditional probability of the prediction error, given the context in which it occurs. This can be done by estimating the probability density function by maintaining counts of symbol occurrences within each context 22 or by estimating the parameters ͑variance for example͒ of an assumed probability density function ͑Laplacian, for example͒ as in Ref.…”

Section: Near-lossless Compression Based On Predictive Coding Techniquesmentioning

confidence: 99%

Near-lossless image compression techniques

Ansari

Memon

Ceran

1998

J. Electron. Imaging

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Abstract. Predictive and multiresolution techniques for nearlossless image compression based on the criterion of maximum allowable deviation of pixel values are investigated. A procedure for near-lossless compression using a modification of lossless predictive coding techniques to satisfy the specified tolerance is described. Simulation results with modified versions of two of the best lossless predictive coding techniques known, CALIC and JPEG-LS, are provided. Application of lossless coding based on reversible transforms in conjunction with prequantization is shown to be inferior to predictive techniques for near-lossless compression. A partial embedding two-layer scheme is proposed in which an embedded multiresolution coder generates a lossy base layer, and a simple but effective context-based lossless coder codes the difference between the original image and the lossy reconstruction. Results show that this lossy plus near-lossless technique yields compression ratios close to those obtained with predictive techniques, while providing the feature of a partially embedded bit-stream. © 1998 SPIE and IS&T. [S1017-9909(98)

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Parameter reduction and context selection for compression of gray-scale images

Cited by 83 publications

References 5 publications

Band ordering in lossless compression of multispectral images

Band ordering in lossless compression of multispectral images

Context-based lossless and near-lossless compression of EEG signals

Near-lossless image compression techniques

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