Context-based, adaptive, lossless image coding

Wu, Xiaolin; Memon, Nasir

doi:10.1109/26.585919

Cited by 868 publications

(533 citation statements)

References 9 publications

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“…• CALIC-A-the relatively complex predictive and adaptive image compression algorithm using arithmetic entropy coder, which because of the very good compression ratios is commonly used as a reference for other image compression algorithms [2,3].…”

Section: Comparison To Other Techniquesmentioning

confidence: 99%

“…Many image compression algorithms, including CALIC [2,3], JPEG-LS [4], and SZIP [5], are predictive, as is the algorithm introduced in this paper. In a predictive algorithm, we use the predictor function to guess the pixel intensities and then we calculate the prediction errors, i.e., differences between actual and predicted pixel intensities.…”

Section: Introductionmentioning

confidence: 99%

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Simple fast and adaptive lossless image compression algorithm

Starosolski

2006

Softw Pract Exp

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In this paper we present a new lossless image compression algorithm. To achieve the high compression speed we use a linear prediction, modified Golomb-Rice code family, and a very fast prediction error modeling method. We compare the algorithm experimentally with others for medical and natural continuous tone grayscale images of depths of up to 16 bits. Its results are especially good for big images, for natural images of high bit depths, and for noisy images. The average compression speed on Intel Xeon 3.06 GHz CPU is 47 MB/s. For big images the speed is over 60 MB/s, i.e., the algorithm needs less than 50 CPU cycles per byte of image.

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Section: Comparison To Other Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simple fast and adaptive lossless image compression algorithm

Starosolski

2006

Softw Pract Exp

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“…State-of-the-art lossless image compression algorithms, such as, JPEG-LS [1], CALIC [2], and EDP [3], have been proposed in the context-adaptive predictive coding framework. The essential components of the framework are prediction followed by entropy coding of the resulting residuals.…”

Section: Introductionmentioning

confidence: 99%

Lossless image coding using hierarchical decomposition and recursive partitioning

Ali

Murshed

Shahriyar

et al. 2016

SIP

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State-of-the-art lossless image compression schemes, such as JPEG-LS and CALIC, have been proposed in the context, have been proposed in the context-adaptive predictive coding framework. The essential components of the framework are prediction followed by entropy coding of the resulting residuals. In the framework, an image is encoded in a predefined order, such as, the raster scan order. It relies on the observation that the intensity value of a pixel is highly correlated with its previously encoded neighboring pixels and thus they can be used to make an effective prediction for the current pixel. The beneficial effect of prediction is that the zero-order entropy of the residual is significantly lower than that of the original image. Therefore, encoding of the residuals, instead of the original images, using entropy coding, such as Huffman codes and arithmetic codes, yields better compression in practice.Entropy coding of the residuals requires knowledge of their probability distribution. It can be observed that the distribution of residuals depends on the image's activity

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“…Indeed, lossless compression is required either from a legal point of view (relative to each country), or from the radiology point of view (to avoid any misdiagnosis) [11]. The most efficient lossless compression schemes, such as CALIC [12] or Edge Directed Prediction based solution [13], do not provide any progressive transmission of the information yet this can be useful for transmissions on low bitrate networks, in particular on the wireless channel. Few schemes are dedicated to medical image compression.…”

Section: Introductionmentioning

confidence: 99%

Joint source–channel coding: Secured and progressive transmission of compressed medical images on the Internet

Babel¹,

Parrein²,

Déforges³

et al. 2008

Computerized Medical Imaging and Graphics

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The joint source-channel coding system proposed in this paper has two aims: lossless compression with a progressive mode and the integrity of medical data, which takes into account the priorities of the image and the properties of a network with no guaranteed quality of service. In this context, the use of scalable coding, Locally Adapted Resolution (LAR) and a discrete and exact Radon transform, known as the Mojette transform, meets this twofold requirement. In this paper, details of this joint coding implementation are provided as well as a performance evaluation with respect to the reference CALIC coding and to unequal error protection using Reed-Solomon codes.

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Context-based, adaptive, lossless image coding

Cited by 868 publications

References 9 publications

Simple fast and adaptive lossless image compression algorithm

Simple fast and adaptive lossless image compression algorithm

Lossless image coding using hierarchical decomposition and recursive partitioning

Joint source–channel coding: Secured and progressive transmission of compressed medical images on the Internet

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